I load the required packages for current analysis.
require(pacman)
p_load(dlookr,patchwork,skimr,kableExtra,naniar,lubridate,purrr,tidyr,
crosstable,flextable,scales,survival,survminer,DataExplorer,performance,
missForest,HDoutliers,caret,praznik,mRMRe,VarSelLCM,cluster,plotly,
ca,factoextra, tidyverse)
# I load the data from csv file:
lung_data <- read.csv('data/complete_clinrad_data.csv', sep = ',') # called complete clin rad data
# I assign Patient ID to rownames to ease further work with the data
rownames(lung_data) <- lung_data$PatientID
#I remove PatientID column an additional index column not of interest as we will use rownames(patient id) to identify each entry
lung_data <- lung_data %>% select(.,-c(X,PatientID))
# I check dataframe dimensions:
dim(lung_data)
## [1] 421 1257
# As many clinical variables are identified as numeric when this is not the case,
# I modify data type for specific variables of interest
# clinical T,N,M and overall stage are ordered categorical variables:
lung_data$clinical.T.Stage <- factor(lung_data$clinical.T.Stage, ordered = TRUE)
lung_data$Clinical.N.Stage <- factor(lung_data$Clinical.N.Stage, ordered = TRUE)
lung_data$Clinical.M.Stage <- factor(lung_data$Clinical.M.Stage, ordered = TRUE)
lung_data$Overall.Stage <- factor(lung_data$Overall.Stage, ordered = TRUE)
# dead status event is a binary categorical variable:
lung_data$deadstatus.event <- factor(lung_data$deadstatus.event)
# gender, histology, and manufacturer are all categorical variables also:
lung_data$gender <- factor(lung_data$gender)
lung_data$Histology <- factor(lung_data$Histology)
lung_data$Manufacturer <- factor(lung_data$Manufacturer)
# study date is a date variable, we adjust format with the help of lubridate package:
lung_data$Study.Date <- mdy(lung_data$Study.Date)
# I check resulting data structure and varnames for the modified variables:
str(lung_data[1:11])
## 'data.frame': 421 obs. of 11 variables:
## $ age : num 78.8 83.8 68.2 70.9 80.5 ...
## $ clinical.T.Stage: Ord.factor w/ 5 levels "1"<"2"<"3"<"4"<..: 2 2 2 2 4 3 2 2 2 4 ...
## $ Clinical.N.Stage: Ord.factor w/ 5 levels "0"<"1"<"2"<"3"<..: 4 1 4 2 3 2 3 3 3 4 ...
## $ Clinical.M.Stage: Ord.factor w/ 3 levels "0"<"1"<"3": 1 1 1 1 1 1 1 1 1 1 ...
## $ Overall.Stage : Ord.factor w/ 4 levels "I"<"II"<"IIIa"<..: 4 1 4 2 4 3 3 3 3 4 ...
## $ Histology : Factor w/ 4 levels "adenocarcinoma",..: 2 4 2 4 4 4 4 1 4 4 ...
## $ gender : Factor w/ 2 levels "female","male": 2 2 2 2 2 2 2 2 2 1 ...
## $ Survival.time : int 2165 155 256 141 353 173 137 77 131 2119 ...
## $ deadstatus.event: Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 1 ...
## $ Study.Date : Date, format: "2008-09-18" "2014-01-01" ...
## $ Manufacturer : Factor w/ 3 levels "CMS Inc.","Plastimatch",..: 3 1 1 3 1 1 3 1 1 1 ...
# We can see the remaining numeric variables account for all the radiomic variables (1246)
dim(lung_data %>% keep(is.numeric) %>% select(.,-c(age,Survival.time)))
## [1] 421 1246
First I do an exploratory analysis to check for missing values in the whole data frame.
table(is.na(lung_data))
##
## FALSE TRUE
## 529131 66
table(is.null(lung_data))
##
## FALSE
## 1
We can see there are 66 missing values in total.
I generate a table that shows within the whole dataframe the variables with a list one missing value and the detail of number of missing values and its completeness rate.
(table1 <- skim(lung_data) %>%
select(skim_variable,n_missing,complete_rate) %>%
filter(n_missing > 0) %>%
kable(full_with = FALSE) %>%
kable_classic_2())
| skim_variable | n_missing | complete_rate |
|---|---|---|
| clinical.T.Stage | 1 | 0.9976247 |
| Overall.Stage | 1 | 0.9976247 |
| Histology | 42 | 0.9002375 |
| age | 22 | 0.9477435 |
table1 %>% save_kable("tables/table1.pdf")
I generate visual representations of single and joint missing values using gg_miss_upset function from naniar package.
tiff(filename = "figures/Fig1.tiff", width = 200, height = 150, units = "mm", res = 300)
gg_miss_upset(lung_data %>% select_if(~ any(is.na(.))))
fig <- dev.off()
gg_miss_upset(lung_data %>% select_if(~ any(is.na(.))))
Overall completeness rate was superior to 0.9 for every variable. Histology is our response variable for our first research question so observations without a value for histology variable will be fully excluded. In addition as a subgroup of NSCLC not otherwise specified (nos) is available within the histology categories it is not clear if NA cases have in effect the confirmation of NSCLC diagnosis at all; so it makes sense to exclude this cases.
lung_data_complete <- lung_data %>% drop_na(Histology)
dim(lung_data_complete)
## [1] 379 1257
After excluding every row with missing histology value, we end up with 379 observations. On the contrary we might imput missing values affecting other variables to avoid missing additional observations that may be usefull, but I will continue with imputation after evaluation data distribution, and data coherence to check if any additional values should be considered as missing and which imputation technique might be pertinent to use.
I verify general statistical summary and distribution of features.
First for study dates:
lung_data_complete %>% select(Study.Date) %>% skim()
| Name | Piped data |
| Number of rows | 379 |
| Number of columns | 1 |
| _______________________ | |
| Column type frequency: | |
| Date | 1 |
| ________________________ | |
| Group variables | None |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| Study.Date | 0 | 1 | 2004-09-27 | 2014-01-01 | 2008-09-07 | 283 |
Then I get summary statistics for factor variables:
(table2a <- lung_data_complete %>%
select(Histology,gender,deadstatus.event,Manufacturer) %>%
univar_category() %>%
kable(full_with = FALSE) %>% kable_classic())
|
|
|
|
table2a %>% save_kable("tables/table2a.pdf")
(table2b <- lung_data_complete %>%
select(Overall.Stage,clinical.T.Stage,Clinical.N.Stage,Clinical.M.Stage) %>%
univar_category() %>%
kable(full_with = FALSE) %>% kable_classic())
|
|
|
|
table2b %>% save_kable("tables/table2b.pdf")
I represent factor variables with barplots to visualize data distribution and clases available for each categorical variable:
lung_data_complete %>%
keep(is.factor) %>%
gather() %>%
ggplot(aes(value)) +
facet_wrap(~ key, scales = "free",ncol = 2) +
geom_bar()+
scale_y_continuous( limits = c(0,400)) +
geom_text(stat='count', aes(label=..count..), vjust=-0.1, size = 7) +
theme(strip.text.x = element_text(size = 18), text = element_text(size=19))
ggsave("figures/Fig2.tiff", scale = 0.4)
We can see from the Overall.Stage variable distribution, that stage IV is not represented in this dataset so we will center analysis mainly on this groups of non-metastatic disease patients. There seems to be some isolated incoherent, erroneous registries regarding clinical T, N and M categories. Possible values for clinical.T.Stage category in lung cancer range from T1 to T4, and as we can see there are few cases with T == 5. Same happens with Clinical.N.Stage == 4 and we also find some cases with Clinical M Stage == 3 that do not exists either within this classicifcation. Specific incoherent cases are enumerated in tables 5.3, 5.4, 5.5 respectively.
(table3 <- lung_data_complete %>%
filter(clinical.T.Stage ==5) %>% select(clinical.T.Stage,Overall.Stage) %>%
kable(full_with = FALSE) %>%
kable_classic_2())
| clinical.T.Stage | Overall.Stage | |
|---|---|---|
| LUNG1-272 | 5 | NA |
table3 %>% save_kable("tables/table3.pdf")
(table4 <- lung_data_complete %>%
filter(Clinical.N.Stage ==4) %>% select(Clinical.N.Stage,Overall.Stage) %>%
kable(full_with = FALSE) %>%
kable_classic_2())
| Clinical.N.Stage | Overall.Stage | |
|---|---|---|
| LUNG1-167 | 4 | IIIb |
| LUNG1-232 | 4 | IIIb |
| LUNG1-292 | 4 | IIIb |
table4 %>% save_kable("tables/table4.pdf")
(table5 <- lung_data_complete %>% filter(Clinical.M.Stage ==3) %>%
select(Clinical.M.Stage,Overall.Stage) %>%
kable(full_with = FALSE) %>%
kable_classic_2())
| Clinical.M.Stage | Overall.Stage | |
|---|---|---|
| LUNG1-072 | 3 | IIIb |
| LUNG1-256 | 3 | IIIb |
| LUNG1-269 | 3 | IIIa |
| LUNG1-333 | 3 | IIIa |
table5 %>% save_kable("tables/table5.pdf")
These are interpreted as erroneous so missing values are assigned to replace these entries as well as for plastimatch manufacturer. This will be then handled along with missing values imputation.
# I assign na values to erroneous entries and drop unused levels:
lung_data_complete <- lung_data_complete %>% replace_with_na(
replace = list(clinical.T.Stage = 5,
Clinical.N.Stage = 4,
Clinical.M.Stage = 3,
Manufacturer = 'Plastimatch')) %>%
droplevels.data.frame()
We can see now the joint missing patterns after removing observations with histology missing values and imputing with missing values the incoherent clinical T/N/M entries:
tiff(filename = "figures/Fig3.tiff", width = 200, height = 150, units = "mm", res = 300)
gg_miss_upset(lung_data_complete %>% select_if(~ any(is.na(.))))
fig <- dev.off()
gg_miss_upset(lung_data_complete %>% select_if(~ any(is.na(.))))
Now I continue with the summary statistics for numeric clinical numeric variables:
(table6 <- lung_data_complete %>%
select(age,Survival.time) %>%
describe() %>%
select(described_variables,n,na,mean,sd,se_mean,IQR,skewness,kurtosis) %>%
kable(full_with = FALSE) %>% kable_classic_2())
| described_variables | n | na | mean | sd | se_mean | IQR | skewness | kurtosis |
|---|---|---|---|---|---|---|---|---|
| age | 366 | 13 | 68.1016 | 10.14966 | 0.5305312 | 14.74875 | -0.3099521 | -0.3094659 |
| Survival.time | 379 | 0 | 983.6464 | 1020.99688 | 52.4450869 | 1126.00000 | 1.4764935 | 1.3393722 |
table6 %>% save_kable("tables/table6.pdf")
And generate histograms to better visualize distributions:
g1 <- ggplot(lung_data_complete,aes(age)) +
geom_histogram(alpha = .8) +
theme_bw()
g2 <- ggplot(lung_data_complete,aes(Survival.time)) +
geom_histogram(alpha = .8) +
theme_bw()
(g1 + g2)
ggsave("figures/Fig4.tiff",width = 174, height = 100, device="tiff", dpi=300, units = "mm", scale = 1)
First as my first research question is weather radiomic variables are related with histology class or not, I want to check histology is independent from other clinical variables and manufacturer of the scanner used to do the exam.
g1 <- ggplot(lung_data_complete,aes(age, fill = Histology)) +
geom_boxplot(alpha = .5) +
coord_flip() +
theme_bw() +
theme(legend.position = 'none')
g2 <- ggplot(lung_data_complete,aes(gender, fill = Histology)) +
geom_bar(position = 'dodge',alpha = .5) +
theme_bw() +
theme(legend.position = 'none')
g3 <- ggplot(lung_data_complete,aes(Overall.Stage, fill = Histology)) +
geom_bar(position = 'dodge',alpha = .5) +
theme_bw()
g4 <- ggplot(lung_data_complete,aes(Manufacturer, fill = Histology)) +
geom_bar(position = 'dodge',alpha = .5) +
theme_bw()
(g1 + g2)/(g3 + g4) + plot_layout(guides = "collect")
ggsave("figures/Fig5.tiff",width = 174, height = 100, device="tiff", dpi=300, units = "mm", scale = 1)
I check the relationship between categorical variables with main variables of interest. I use crosstable package to ease table construction and automatic test to search for independency.
# As we have few observations for N1 group, I will have to join two groups to
# be able to test independence with histology
table(lung_data_complete$Histology,lung_data_complete$Clinical.N.Stage)
##
## 0 1 2 3
## adenocarcinoma 18 4 15 14
## large cell 32 5 48 28
## nos 27 0 21 14
## squamous cell carcinoma 63 12 50 25
levels(lung_data_complete$Clinical.N.Stage) <- c("0","1_2","1_2","3")
(table7 <- crosstable(lung_data_complete, c(age, gender, clinical.T.Stage,
Clinical.N.Stage,
Overall.Stage, Manufacturer),
by = Histology, test = TRUE,
showNA = 'no') %>% as_flextable() %>% autofit())
label | variable | Histology | test | |||
adenocarcinoma | large cell | nos | squamous cell carcinoma | |||
age | Min / Max | 45.7 / 85.6 | 42.5 / 91.7 | 43.3 / 82.4 | 33.7 / 88.4 | p value: 0.0076 |
Med [IQR] | 68.1 [60.2;74.8] | 67.3 [59.6;74.1] | 67.2 [58.7;74.9] | 70.5 [64.3;77.9] | ||
Mean (std) | 67.3 (9.6) | 66.9 (10.2) | 65.6 (10.3) | 70.2 (9.9) | ||
N (NA) | 49 (2) | 110 (4) | 58 (4) | 149 (3) | ||
gender | female | 19 (15.97%) | 43 (36.13%) | 17 (14.29%) | 40 (33.61%) | p value: 0.1574 |
male | 32 (12.31%) | 71 (27.31%) | 45 (17.31%) | 112 (43.08%) | ||
clinical.T.Stage | 1 | 12 (17.39%) | 18 (26.09%) | 14 (20.29%) | 25 (36.23%) | p value: 0.4042 |
2 | 22 (14.97%) | 47 (31.97%) | 23 (15.65%) | 55 (37.41%) | ||
3 | 9 (18.00%) | 11 (22.00%) | 7 (14.00%) | 23 (46.00%) | ||
4 | 8 (7.21%) | 37 (33.33%) | 17 (15.32%) | 49 (44.14%) | ||
Clinical.N.Stage | 0 | 18 (12.86%) | 32 (22.86%) | 27 (19.29%) | 63 (45.00%) | p value: 0.1737 |
1_2 | 19 (12.26%) | 53 (34.19%) | 21 (13.55%) | 62 (40.00%) | ||
3 | 14 (17.28%) | 28 (34.57%) | 14 (17.28%) | 25 (30.86%) | ||
Overall.Stage | I | 11 (16.67%) | 15 (22.73%) | 17 (25.76%) | 23 (34.85%) | p value: 0.0117 |
II | 8 (21.05%) | 5 (13.16%) | 2 (5.26%) | 23 (60.53%) | ||
IIIa | 14 (12.96%) | 36 (33.33%) | 14 (12.96%) | 44 (40.74%) | ||
IIIb | 18 (10.84%) | 57 (34.34%) | 29 (17.47%) | 62 (37.35%) | ||
Manufacturer | CMS Inc. | 12 (13.95%) | 21 (24.42%) | 20 (23.26%) | 33 (38.37%) | p value: 0.2199 |
SIEMENS | 39 (13.36%) | 92 (31.51%) | 42 (14.38%) | 119 (40.75%) | ||
table7 %>% save_as_image("tables/table7.png")
## [1] "/Users/mechyserra/Drive_SerraMM/Master_BioinformaticayBioestadistica/Trabajo_Fin_de_Master/TFM/TFM_MSerra/TFM_MSERRA_2022/tables/table7.png"
(table8 <- crosstable(lung_data_complete, c(age, gender,
Manufacturer),
by = Overall.Stage, test = TRUE,
showNA = 'no') %>% as_flextable() %>% autofit())
label | variable | Overall.Stage | test | |||
I | II | IIIa | IIIb | |||
age | Min / Max | 51.7 / 91.7 | 50.2 / 87.0 | 33.7 / 85.1 | 42.5 / 88.4 | p value: <0.0001 |
Med [IQR] | 75.7 [68.5;80.2] | 73.4 [70.2;78.6] | 67.1 [60.3;74.1] | 65.8 [59.9;72.8] | ||
Mean (std) | 74.1 (9.0) | 72.9 (9.1) | 66.7 (10.4) | 65.8 (9.4) | ||
N (NA) | 62 (4) | 35 (3) | 106 (2) | 162 (4) | ||
gender | female | 19 (15.97%) | 9 (7.56%) | 38 (31.93%) | 53 (44.54%) | p value: 0.5734 |
male | 47 (18.15%) | 29 (11.20%) | 70 (27.03%) | 113 (43.63%) | ||
Manufacturer | CMS Inc. | 22 (25.58%) | 6 (6.98%) | 21 (24.42%) | 37 (43.02%) | p value: 0.0987 |
SIEMENS | 43 (14.78%) | 32 (11.00%) | 87 (29.90%) | 129 (44.33%) | ||
table8 %>% save_as_image("tables/table8.png")
## [1] "/Users/mechyserra/Drive_SerraMM/Master_BioinformaticayBioestadistica/Trabajo_Fin_de_Master/TFM/TFM_MSerra/TFM_MSERRA_2022/tables/table8.png"
In this tables we can easily see how gender and histology, and manufacturer and histology are independent. On the contrary, there seems to be some relationship between histology group and age, as well as between histology and overall stage. In this case it seems specially important to verify the relationship of these variables with any clustering model generated as these could bias interpretation of clustering association to target histology variable.
Though I did not identify here a relationship between histology class and T or N stage, we know this are dependent with overall stage for which we found association to histology class, so we should be careful with this when interpreting model results.
I continue verifying the relationship between ordinal variables using Spearman correlation coeficient
(table9 <- lung_data_complete %>%
select(Overall.Stage,clinical.T.Stage) %>%
drop_na(Overall.Stage,clinical.T.Stage) %>%
table()%>%
addmargins() %>%
kable(full_with = FALSE) %>% kable_classic_2() %>%
add_header_above(c("Overall.Stage" = 1, "Clinical.T.Stage" = 5)) )
|
Overall.Stage
|
Clinical.T.Stage
|
||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | Sum | |
| I | 27 | 39 | 0 | 0 | 66 |
| II | 3 | 18 | 17 | 0 | 38 |
| IIIa | 26 | 55 | 27 | 0 | 108 |
| IIIb | 13 | 35 | 6 | 111 | 165 |
| Sum | 69 | 147 | 50 | 111 | 377 |
table9 %>% save_kable("tables/table9.pdf")
cor(as.integer(lung_data_complete$clinical.T.Stage),
as.integer(lung_data_complete$Overall.Stage),
method = 'spearman',
use = 'complete.obs')
## [1] 0.5841231
(table10 <- lung_data_complete %>%
select(Overall.Stage,Clinical.N.Stage) %>%
drop_na(Overall.Stage,Clinical.N.Stage) %>%
table()%>%
addmargins() %>%
kable(full_width = FALSE) %>%
kable_classic_2() %>%
add_header_above(c("Overall.Stage" = 1, "Clinical.N.Stage" = 4)))
|
Overall.Stage
|
Clinical.N.Stage
|
|||
|---|---|---|---|---|
| 0 | 1_2 | 3 | Sum | |
| I | 65 | 1 | 0 | 66 |
| II | 25 | 13 | 0 | 38 |
| IIIa | 0 | 100 | 8 | 108 |
| IIIb | 50 | 40 | 73 | 163 |
| Sum | 140 | 154 | 81 | 375 |
table10 %>% save_kable("tables/table10.pdf")
cor(as.integer(lung_data_complete$Clinical.N.Stage),
as.integer(lung_data_complete$Overall.Stage),
method = 'spearman',
use = 'complete.obs')
## [1] 0.5067627
(table11 <- lung_data_complete %>%
select(clinical.T.Stage,Clinical.N.Stage) %>%
drop_na(clinical.T.Stage,Clinical.N.Stage) %>%
table()%>%
addmargins() %>%
kable(full_width = FALSE) %>%
kable_classic_2() %>%
add_header_above(c("Clinical.T.Stage" = 1, "Clinical.N.Stage" = 4)))
|
Clinical.T.Stage
|
Clinical.N.Stage
|
|||
|---|---|---|---|---|
| 0 | 1_2 | 3 | Sum | |
| 1 | 28 | 24 | 17 | 69 |
| 2 | 46 | 66 | 35 | 147 |
| 3 | 17 | 24 | 9 | 50 |
| 4 | 49 | 40 | 19 | 108 |
| Sum | 140 | 154 | 80 | 374 |
table11 %>% save_kable("tables/table11.pdf")
cor(as.integer(lung_data_complete$Clinical.N.Stage),
as.integer(lung_data_complete$clinical.T.Stage),
method = 'spearman',
use = 'complete.obs')
## [1] -0.07690129
As expected we see there is some degree of correlation between Overall Stage and T and N clinical categories. On the contrary T and N categories do not show a high degree of correlation.
I check the distribution of different histology classes along study dates to check if there might be any suggestion of potential time batch bias.
lung_data_complete$Study.Date <- as.POSIXct(lung_data_complete$Study.Date)
ggplot(lung_data_complete, aes(Study.Date, ..count.., fill = Histology)) +
geom_histogram(alpha = .5) +
theme_bw() + xlab(NULL) +
scale_x_datetime(breaks = date_breaks("3 months"),
labels = date_format("%Y-%b")) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
ggsave("figures/Fig6.tiff",width = 174, height = 100, device="tiff", dpi=300, units = "mm", scale = 1)
Regarding the period of time during which studies were performed, we see all classes seem pretty much represented along the years in favor of avoiding specific technique batch biases.
Now I want to check if there is a relationship between survival and main categorical classes. To do so I will use Survival and survminer R packages to construct Kaplan Meyer plots using Survival.time and deadstatus.event variables and compare median survival between different classes.
First I describe general survival probability for the whole group.
tiff(filename = "figures/Fig7.1.tiff", width = 300, height = 200, units = "mm", res = 300)
ggsurvplot(survfit(Surv(Survival.time, as.numeric(deadstatus.event)) ~ 1, data = lung_data_complete),
conf.int=TRUE,
surv.median.line = 'hv',
pval=TRUE,
risk.table='percentage',
tables.height = 0.2)
fig <- dev.off()
ggsurvplot(survfit(Surv(Survival.time, as.numeric(deadstatus.event)) ~ 1, data = lung_data_complete),
conf.int=TRUE,
surv.median.line = 'hv',
pval=TRUE,
risk.table='percentage',
tables.height = 0.2)
survfit(Surv(Survival.time, as.numeric(deadstatus.event)) ~ 1, data = lung_data_complete)
## Call: survfit(formula = Surv(Survival.time, as.numeric(deadstatus.event)) ~
## 1, data = lung_data_complete)
##
## n events median 0.95LCL 0.95UCL
## [1,] 379 336 573 492 660
One year probability survival was 0.64 when evaluating the whole group (95% CI: 0.597 - 0.694).
tiff(filename = "figures/Fig7.2.tiff", width = 300, height = 200, units = "mm", res = 300)
ggsurvplot(survfit(Surv(Survival.time, as.numeric(deadstatus.event)) ~ Histology, data = lung_data_complete),
conf.int=TRUE,
surv.median.line = 'hv',
pval=TRUE,
risk.table='percentage',
tables.height = 0.2)
fig <- dev.off()
ggsurvplot(survfit(Surv(Survival.time, as.numeric(deadstatus.event)) ~ Histology, data = lung_data_complete),
conf.int=TRUE,
surv.median.line = 'hv',
pval=TRUE,
risk.table='percentage',
tables.height = 0.2)
tiff(filename = "figures/Fig8.tiff", width = 300, height = 200, units = "mm", res = 300)
ggsurvplot(survfit(Surv(Survival.time, as.numeric(deadstatus.event)) ~ Overall.Stage, data = lung_data_complete),
conf.int=TRUE,
surv.median.line = 'hv',
pval=TRUE,
risk.table='percentage',
tables.height = 0.2)
fig <- dev.off()
ggsurvplot(survfit(Surv(Survival.time, as.numeric(deadstatus.event)) ~ Overall.Stage, data = lung_data_complete),
conf.int=TRUE,
surv.median.line = 'hv',
pval=TRUE,
risk.table='percentage',
tables.height = 0.2)
tiff(filename = "figures/Fig9.tiff", width = 300, height = 200, units = "mm", res = 300)
ggsurvplot(survfit(Surv(Survival.time, as.numeric(deadstatus.event)) ~ gender, data = lung_data_complete),
conf.int=TRUE,
surv.median.line = 'hv',
pval=TRUE,
risk.table='percentage',
surv.plot.height = 0.7,
tables.height = 0.2)
fig <- dev.off()
ggsurvplot(survfit(Surv(Survival.time, as.numeric(deadstatus.event)) ~ gender, data = lung_data_complete),
conf.int=TRUE,
surv.median.line = 'hv',
pval=TRUE,
risk.table='percentage',
surv.plot.height = 0.7,
tables.height = 0.2)
tiff(filename = "figures/Fig10.tiff", width = 300, height = 200, units = "mm", res = 300)
ggsurvplot(survfit(Surv(Survival.time, as.numeric(deadstatus.event)) ~ Manufacturer, data = lung_data_complete),
conf.int=TRUE,
surv.median.line = 'hv',
pval=TRUE,
risk.table='percentage',
surv.plot.height = 0.7,
tables.height = 0.3)
fig <- dev.off()
ggsurvplot(survfit(Surv(Survival.time, as.numeric(deadstatus.event)) ~ Manufacturer, data = lung_data_complete),
conf.int=TRUE,
surv.median.line = 'hv',
pval=TRUE,
risk.table='percentage',
surv.plot.height = 0.7,
tables.height = 0.3)
sd <- survdiff(Surv(Survival.time, as.numeric(deadstatus.event)) ~ Manufacturer, data = lung_data_complete)
1 - pchisq(sd$chisq, length(sd$n) - 1)
## [1] 0.002405843
survfit(Surv(Survival.time, as.numeric(deadstatus.event)) ~ Manufacturer, data = lung_data_complete)
## Call: survfit(formula = Surv(Survival.time, as.numeric(deadstatus.event)) ~
## Manufacturer, data = lung_data_complete)
##
## 1 observation deleted due to missingness
## n events median 0.95LCL 0.95UCL
## Manufacturer=CMS Inc. 86 79 444 325 597
## Manufacturer=SIEMENS 292 256 617 522 704
While there is no significant relationship between Survival and Histology, Survival and Overall Stage, and Survival and Gender; there is a finding of major importance here regarding observations corresponding to scanners from different Manufacturer. This could add a major bias to the model so we have to exclude any radiomic features related to scanner Manufacturer to make sure to remove any bias from this source.
As there are so many continuous radiomic variables I will just check for the moment their pearson correlation coefficient and represent this with heatmap. After performing variable selection I will further analyze radiomic variables distribution and relationship with other clinical variables.
radiomic_vars <- lung_data_complete %>% keep(is.numeric) %>%
select(.,-c(age,Survival.time))
plot_correlation(radiomic_vars, type = "c", theme_config = list("legend.position" = "bottom","axis.text.x" = element_blank(), "axis.text.y" = element_blank()))
ggsave("figures/Fig11.tiff",width = 200, height = 200, device="tiff", dpi=300, units = "mm", scale = 1)
Now I will perform a Shapiro Wilk test to formaly evaluate normality for every numeric variable available in current data frame:
# I apply shapiro wilk test to every numeric value in the data frame
shap <- lung_data_complete %>%
keep(is.numeric) %>%
apply(.,2,shapiro.test)
# I define a vector with a boolean stating if p value is greater than corrected 0.05
shapres <- sapply(shap, function(x) x$p.value > .05/length(shap))
table(shapres)
## shapres
## FALSE TRUE
## 1081 167
When exploring numerical variable distribution we could already see many variables with an assymetric distribution mostly non-normally distributed variables. We will take this into account when selecting outlier detection techniques and imputation techniques both for outliers and missing values.
To perform outlier detection I will use HDoutliers package that allows performing multivariate outlier detection for mixed data. To be able to work with this algorithm I need to perform imputation of missing values first so I will use missForest package that gives me the possibility to perform missing value imputation, again taking in account the mixed data types we are dealing with.
I perform missing values imputation first:
lung_data.imp <- missForest(lung_data_complete %>% select(.,-Study.Date))
#check imputation error. NRMSE is normalized mean squared error. It is used to represent error derived
# from imputing continuous values. PFC (proportion of falsely classified) is used to represent error
# derived from imputing categorical values.
lung_data.imp$OOBerror
## NRMSE PFC
## 4.405347e-12 2.035274e-01
#check new values
lung_data.imp$ximp[rownames(lung_data.imp$ximp) %in% rownames(which(is.na(lung_data_complete), arr.ind=TRUE)),] %>% select(age,gender,clinical.T.Stage,Clinical.N.Stage,Clinical.M.Stage,Overall.Stage,deadstatus.event,Manufacturer)
## age gender clinical.T.Stage Clinical.N.Stage Clinical.M.Stage
## LUNG1-058 82.27790 male 2 0 0
## LUNG1-072 71.47430 male 4 3 0
## LUNG1-085 53.65910 female 2 3 0
## LUNG1-149 65.13863 male 3 3 0
## LUNG1-167 74.92950 male 4 3 0
## LUNG1-174 67.12307 male 1 0 0
## LUNG1-232 73.28680 male 4 1_2 0
## LUNG1-256 53.08420 male 4 1_2 0
## LUNG1-269 73.05950 male 3 3 0
## LUNG1-270 66.06424 male 1 1_2 0
## LUNG1-272 60.13960 male 2 1_2 0
## LUNG1-275 68.08084 male 2 3 0
## LUNG1-292 66.21490 male 4 0 0
## LUNG1-299 71.19469 male 1 0 0
## LUNG1-303 71.75067 male 2 0 0
## LUNG1-307 67.04930 female 1 0 0
## LUNG1-308 64.32323 female 2 1_2 0
## LUNG1-333 63.69880 male 1 1_2 0
## LUNG1-339 65.37640 male 4 1_2 0
## LUNG1-341 70.53138 male 2 0 0
## LUNG1-349 63.42565 male 2 1_2 0
## LUNG1-354 65.10709 female 1 1_2 0
## LUNG1-385 65.56783 male 2 0 0
## Overall.Stage deadstatus.event Manufacturer
## LUNG1-058 I 1 SIEMENS
## LUNG1-072 IIIb 1 SIEMENS
## LUNG1-085 IIIb 1 CMS Inc.
## LUNG1-149 IIIb 1 CMS Inc.
## LUNG1-167 IIIb 1 SIEMENS
## LUNG1-174 I 1 SIEMENS
## LUNG1-232 IIIb 1 SIEMENS
## LUNG1-256 IIIb 1 SIEMENS
## LUNG1-269 IIIa 1 SIEMENS
## LUNG1-270 IIIa 1 SIEMENS
## LUNG1-272 IIIb 1 SIEMENS
## LUNG1-275 IIIb 1 SIEMENS
## LUNG1-292 IIIb 1 SIEMENS
## LUNG1-299 IIIb 1 SIEMENS
## LUNG1-303 I 1 SIEMENS
## LUNG1-307 I 1 SIEMENS
## LUNG1-308 II 1 SIEMENS
## LUNG1-333 IIIa 1 SIEMENS
## LUNG1-339 IIIb 1 SIEMENS
## LUNG1-341 I 1 SIEMENS
## LUNG1-349 II 1 SIEMENS
## LUNG1-354 IIIa 1 SIEMENS
## LUNG1-385 II 1 SIEMENS
# we can see now M stage is in fact 0 for every entry, so we will remove this column
table(lung_data.imp$ximp$Clinical.M.Stage)
##
## 0
## 379
# I remove Clinical M Stage from the dataset and I add Study Date again
lung_data_naimp <- lung_data.imp$ximp %>% select(.,-Clinical.M.Stage)
# Transforms the data according to the specifications in Wilkinson’s hdoutliers algorithm replaces each categorical variables with a numeric variable corresponding to its first component in multiple correspondence analysis, then maps the data to the unit square
lung_data.trans <- dataTrans(lung_data_naimp)
# Implements the first stage of the hdoutliers Algorithm, in which the data is partitioned according to exemplars and their associated lists of members.
lung_data.mem <- getHDmembers(lung_data.trans)
# An exponential distribution is fitted to the upper tail of the nearest-neighbor distances between
# exemplars (the observations considered representatives of each component of member
lung_data.out <- getHDoutliers(lung_data.trans, lung_data.mem, alpha = 0.05)
# Produces a plot of the data (transformed according to the Wilkinson’s specifications) showing the
# outliers. If the data has more than two dimensions, it is plotted onto the principal components of
# the data that remains after removing outliers.
tiff(filename = "figures/Fig12.tiff", width = 200, height = 150, units = "mm", res = 300)
plotHDoutliers(lung_data_naimp,lung_data.out)
fig <- dev.off()
plotHDoutliers(lung_data_naimp,lung_data.out)
# we identify the specific observations classified as outliers:
rownames(lung_data.trans)[c(lung_data.out)]
## [1] "LUNG1-027" "LUNG1-069"
When taking in account all the categorical and numerical variables available on the data set, excluding dates, 2 particular observations are considered outliers, “LUNG1-027” and “LUNG1-069”. Extreme values for a single variable seem to make more sense when they are explained by other variables.
I compare with stray library that has a modified version of hdoutliers, taking only numeric variables, and get the same result:
library(stray)
stray_out <- find_HDoutliers(lung_data_naimp %>% keep(is.numeric),alpha = 0.05)
rownames(lung_data.trans)[c(stray_out$outliers)]
## [1] "LUNG1-027" "LUNG1-069"
I remove the outliers detected:
# I add study date variable again to the update data set
lung_data_no_na <- lung_data_naimp %>% cbind(Study.Date = lung_data_complete$Study.Date)
lung_data_no_out <- lung_data_no_na[-c(which(rownames(lung_data_no_na) %in% rownames(lung_data.trans)[c(lung_data.out)])),]
dim(lung_data_no_out)
## [1] 377 1256
# I build a dataframe just with the radiomic features:
radiomic_vars <- lung_data_no_out %>% keep(is.numeric) %>%
select(.,-c(age,Survival.time))
# I apply shapiro wilk test to every numeric value in the data frame
wilcox <- radiomic_vars %>% apply(.,2,function(x){wilcox.test(x~lung_data_no_out$Manufacturer)})
# I define a vector with a boolean stating if p value is greater than corrected 0.05
wilcoxres <- sapply(wilcox, function(x){x$p.value > .05})
table(wilcoxres)
## wilcoxres
## FALSE TRUE
## 532 714
stable_radiomic_vars <- radiomic_vars %>% select(.,-all_of(names(wilcoxres[wilcoxres == TRUE])))
dim(stable_radiomic_vars)
## [1] 377 532
library(caret)
names(stable_radiomic_vars)[nearZeroVar(radiomic_vars)]
## character(0)
No near 0 variables
I first use maximum relevance minimum redundance algorithm. As there are so many radiomic features, I want to be sure I preserve most relevant features for the classes of interest while filtering redundant variables. To do so I use MRMR function from praznik package, and mRMR.classic function from mRMRe package.
# I first select MRMR features taking in account histology class:
select1 <- MRMR(stable_radiomic_vars,lung_data_no_out$Histology,k = 20)
names(select1$selection)
## [1] "original_shape_VoxelVolume"
## [2] "log.sigma.4.0.mm.3D_glszm_LargeAreaEmphasis"
## [3] "wavelet.HHL_glcm_ClusterProminence"
## [4] "log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis"
## [5] "log.sigma.2.0.mm.3D_glcm_Autocorrelation"
## [6] "log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis"
## [7] "wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis"
## [8] "wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis"
## [9] "log.sigma.4.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis"
## [10] "wavelet.HHH_glszm_GrayLevelNonUniformity"
## [11] "wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis"
## [12] "wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis"
## [13] "log.sigma.5.0.mm.3D_glszm_LargeAreaEmphasis"
## [14] "wavelet.HHH_glcm_ClusterProminence"
## [15] "wavelet.HHL_glrlm_ShortRunHighGrayLevelEmphasis"
## [16] "wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis"
## [17] "wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis"
## [18] "wavelet.LLL_glcm_ClusterProminence"
## [19] "wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis"
## [20] "log.sigma.4.0.mm.3D_glszm_ZoneVariance"
# Then I do the same for the survival target interest
df <- as.data.frame(stable_radiomic_vars %>% cbind(surv1 = Surv(lung_data_no_out$Survival.time, lung_data_no_out$deadstatus.event)))
df$original_shape_VoxelVolume <- as.numeric(df$original_shape_VoxelVolume)
dd <- mRMR.data(data = df)
cat('index_surv:' ,grep("surv1", colnames(df)))
## index_surv: 533
select2 <- mRMR.classic(data = dd, target_indices = c(grep("surv1", colnames(df))), feature_count = 20)
selected2_features <- select2@feature_names[solutions(select2)$'533'[,1]]
selected2_features
## [1] "wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis"
## [2] "log.sigma.4.0.mm.3D_glszm_ZoneVariance"
## [3] "wavelet.HHH_glrlm_GrayLevelVariance"
## [4] "log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized"
## [5] "wavelet.HHH_glcm_Imc1"
## [6] "original_glcm_Imc1"
## [7] "log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis"
## [8] "wavelet.LHH_glszm_GrayLevelNonUniformity"
## [9] "wavelet.LHH_firstorder_Skewness"
## [10] "wavelet.HHH_glszm_GrayLevelNonUniformityNormalized"
## [11] "wavelet.LHH_glcm_Imc1"
## [12] "log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis"
## [13] "log.sigma.5.0.mm.3D_firstorder_Kurtosis"
## [14] "log.sigma.5.0.mm.3D_firstorder_90Percentile"
## [15] "wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis"
## [16] "wavelet.HLH_glcm_DifferenceAverage"
## [17] "wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis"
## [18] "wavelet.HHH_firstorder_RootMeanSquared"
## [19] "wavelet.LHL_gldm_DependenceVariance"
## [20] "wavelet.HHH_glcm_ClusterProminence"
# I check variables matching for both vectors of selected variables
intersect(names(select1$selection),selected2_features)
## [1] "log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis"
## [2] "wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis"
## [3] "wavelet.HHH_glcm_ClusterProminence"
## [4] "log.sigma.4.0.mm.3D_glszm_ZoneVariance"
# Still keep all the variables selected for at least one class taking in account a variable might be highly related to histology class for example and not to survival and so on
mrmr_radiomics <- stable_radiomic_vars %>% dplyr::select(names(select1$selection),all_of(selected2_features))
plot_correlation(mrmr_radiomics, type = "c", theme_config = list(legend.position = "bottom", axis.text.x = element_text(angle =
90, size = 9), axis.text.y = element_text(size = 9)))
ggsave("figures/Fig13.tiff",width = 130, height = 130, device="tiff", dpi=300, units = "mm", scale = 1)
dim(mrmr_radiomics)
## [1] 377 36
We can see dim(mrmr_radiomics)[2] radiomic features were selected taking in account some minimal overlap between both MRMR selections regarding histology or survival.
Then I eliminate still highly redundant variables using findCorrelation function from caret package.
# As after MRMR I still have blocks of highly correlated variables I add an additional simple correlation filter to eliminate those still highly correlated:
redundant_vars <- findCorrelation(
cor(mrmr_radiomics),
cutoff = 0.95,
names = TRUE
)
redundant_vars
## [1] "wavelet.HHL_glrlm_ShortRunHighGrayLevelEmphasis"
## [2] "log.sigma.5.0.mm.3D_glszm_LargeAreaEmphasis"
## [3] "log.sigma.4.0.mm.3D_glszm_LargeAreaEmphasis"
## [4] "log.sigma.4.0.mm.3D_glszm_ZoneVariance"
## [5] "log.sigma.4.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis"
filtered_radiomics <- mrmr_radiomics %>% dplyr::select(.,-all_of(redundant_vars))
plot_correlation(filtered_radiomics, type = "c")
ggsave("figures/Fig14.tiff",width = 130, height = 130, device="tiff", dpi=300, units = "mm", scale = 1)
dim(filtered_radiomics)
## [1] 377 31
We are now left with dim(filtered_radiomics)[2] radiomic features.
(table12 <- filtered_radiomics %>% describe() %>%
select(described_variables,mean,sd,IQR,skewness,kurtosis) %>%
kable(full_with = FALSE) %>% kable_classic_2())
| described_variables | mean | sd | IQR | skewness | kurtosis |
|---|---|---|---|---|---|
| original_shape_VoxelVolume | 7.514392e+04 | 9.164034e+04 | 9.800700e+04 | 2.0851762 | 4.8456596 |
| wavelet.HHL_glcm_ClusterProminence | 7.665988e+01 | 1.454862e+02 | 7.228063e+01 | 8.3455080 | 103.1677055 |
| log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis | 1.019452e+04 | 4.332770e+04 | 3.566937e+03 | 9.0663774 | 102.3326825 |
| log.sigma.2.0.mm.3D_glcm_Autocorrelation | 3.906822e+02 | 4.635002e+02 | 1.964977e+02 | 7.0022832 | 64.9811527 |
| log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis | 2.035400e-03 | 2.590600e-03 | 8.596000e-04 | 6.2617573 | 50.2486161 |
| wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis | 1.386738e+08 | 4.014961e+08 | 9.273493e+07 | 6.7919498 | 63.6744640 |
| wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis | 4.974261e+00 | 7.463963e+00 | 5.531913e+00 | 10.3666533 | 156.5331460 |
| wavelet.HHH_glszm_GrayLevelNonUniformity | 6.279034e+00 | 1.078154e+01 | 4.444444e+00 | 5.5080663 | 37.7984974 |
| wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis | 2.202244e+03 | 3.333935e+03 | 1.541908e+03 | 6.3181067 | 54.5274282 |
| wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis | 6.336000e-04 | 9.168000e-04 | 4.890000e-04 | 4.8892521 | 35.0606807 |
| wavelet.HHH_glcm_ClusterProminence | 5.201669e-01 | 2.434530e-02 | 2.194800e-02 | 7.6931019 | 100.7114041 |
| wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis | 8.932846e+02 | 1.224833e+03 | 6.660940e+02 | 6.0260294 | 44.3592347 |
| wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis | 2.029759e+09 | 5.437150e+09 | 1.779997e+09 | 6.6191390 | 54.5731854 |
| wavelet.LLL_glcm_ClusterProminence | 4.011467e+07 | 5.602678e+07 | 3.576071e+07 | 4.2809018 | 26.1167309 |
| wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis | 1.579710e-02 | 1.467100e-02 | 1.358980e-02 | 3.4688959 | 22.4729366 |
| wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis | 1.426500e-03 | 1.429000e-03 | 1.121800e-03 | 2.3362021 | 6.1842749 |
| wavelet.HHH_glrlm_GrayLevelVariance | 2.508655e-01 | 2.168600e-03 | 7.639000e-04 | 4.8123946 | 29.9377695 |
| log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized | 1.295846e-01 | 2.895060e-02 | 3.887950e-02 | 0.0035690 | 0.1889044 |
| wavelet.HHH_glcm_Imc1 | -1.806540e-02 | 3.470200e-03 | 4.504700e-03 | -1.0817541 | 1.2105680 |
| original_glcm_Imc1 | -2.123565e-01 | 4.045460e-02 | 5.235530e-02 | -0.4524797 | 1.5201208 |
| log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis | 2.179683e+03 | 3.172775e+03 | 2.061257e+03 | 5.0438332 | 34.4480218 |
| wavelet.LHH_glszm_GrayLevelNonUniformity | 1.303759e+02 | 1.629441e+02 | 1.371509e+02 | 3.2948811 | 16.5711634 |
| wavelet.LHH_firstorder_Skewness | 9.730470e-02 | 2.125665e-01 | 1.816981e-01 | 1.0791018 | 8.8925641 |
| wavelet.HHH_glszm_GrayLevelNonUniformityNormalized | 4.580535e-01 | 1.130034e-01 | 1.953485e-01 | -0.0644575 | -0.7458533 |
| wavelet.LHH_glcm_Imc1 | -5.209010e-02 | 9.000900e-03 | 1.068450e-02 | -1.0971085 | 2.1923760 |
| log.sigma.5.0.mm.3D_firstorder_Kurtosis | 3.690143e+00 | 1.856490e+00 | 1.379258e+00 | 3.6248597 | 21.7097063 |
| log.sigma.5.0.mm.3D_firstorder_90Percentile | 1.147649e+01 | 7.613904e+01 | 6.642061e+01 | 0.5149007 | 3.4489701 |
| wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis | 7.941480e-02 | 6.270540e-02 | 4.685890e-02 | 2.1255940 | 4.6026079 |
| wavelet.HLH_glcm_DifferenceAverage | 4.690893e-01 | 4.681940e-02 | 5.090000e-02 | 1.7774220 | 5.4619428 |
| wavelet.HHH_firstorder_RootMeanSquared | 9.999963e+02 | 2.351280e-02 | 1.363290e-02 | -3.6488720 | 19.5431225 |
| wavelet.LHL_gldm_DependenceVariance | 1.432114e+01 | 7.129074e+00 | 1.065779e+01 | 0.5921853 | -0.2546894 |
table12 %>% save_kable("tables/table12.pdf")
In this table we can see summary statistics for selected radiomic features, we can see many of these include negative values, something to take into account when evaluating data transformation.
I further represent univariate distribution by representing data with histograms:
filtered_radiomics %>%
gather() %>%
ggplot(aes(value)) +
facet_wrap(~ key, scales = "free", ncol = 3) +
geom_histogram() +
theme(strip.text.x = element_text(size = 50), text = element_text(size=50))+
theme_minimal()
ggsave("figures/Fig15.tiff",width = 200, height = 300, device="tiff", dpi=300, units = "mm", scale = 1)
And I further evaluate normality by generating qqplot for these selected features.
filtered_radiomics %>%
gather() %>%
ggplot(aes(sample=value)) +
facet_wrap(~ key, scales = "free", ncol = 4) +
stat_qq() +
theme_minimal() +
theme(strip.text.x = element_text(size = 5), text = element_text(size=10))
ggsave("figures/Fig16.tiff",width = 200, height = 300, device="tiff", dpi=300, units = "mm", scale = 1)
As previously evaluated for the whole dataset with Shapiro Wilk test, we see in both histograms and qqplots that many radiomic variables deviate from normality and have a rather skewed distribution. We should take this into account as data transformation may be needed for optimal performance of different model-based cluster models.
As we have mainly positive skewed distributions I will apply a log transform after adding a mimimum value to avoid negative and ease optimization of different models.
min(filtered_radiomics)
## [1] -239.872
log_filtered_radiomics <- filtered_radiomics %>%
lapply(function(x){log(x+241)}) %>%
as.data.frame()
log_filtered_radiomics %>%
gather() %>%
ggplot(aes(sample=value)) +
facet_wrap(~ key, scales = "free", ncol = 4) +
stat_qq() +
theme_minimal() +
theme(strip.text.x = element_text(size = 5), text = element_text(size=10))
ggsave("figures/Fig17.tiff",width = 200, height = 300, device="tiff", dpi=300, units = "mm", scale = 1)
Now that we have a few selected features, I can deepen radiomic feature description missing on initial global analysis.
We continue with evaluation of selected radiomic feature relationship to pertinent target variables. First I generate boxplots to represent radiomic feature distribution for the different histology classes.
I then evaluate mean and standard deviation of radiomic features within each class and test to verify if any of this relationships might be significant.
radiomics_hist <- log_filtered_radiomics %>% cbind(Histology = lung_data_no_out$Histology)
crostab <- crosstable(num_digits = 3,radiomics_hist, c(colnames(radiomics_hist %>% dplyr::select(.,-Histology))), by = Histology, test = TRUE)
(table13 <- crostab %>% separate(test,
into = c('pvalue',
'test'),
sep = "\n") %>%
separate(pvalue,
into = c('string',
'pval'),
sep = ":",
convert = TRUE) %>%
arrange(pval) %>%
filter(variable == 'Mean (std)') %>%
dplyr::select(.,-c(string,.id, test)) %>% kable(full_with = FALSE) %>% kable_classic_2())
| label | variable | adenocarcinoma | large cell | nos | squamous cell carcinoma | pval |
|---|---|---|---|---|---|---|
| wavelet.LHL_gldm_DependenceVariance | Mean (std) | 5.541 (0.028) | 5.536 (0.024) | 5.537 (0.024) | 5.550 (0.030) | 0.0012 |
| wavelet.HHH_glcm_ClusterProminence | Mean (std) | 5.487 (2e-04) | 5.487 (6e-05) | 5.487 (7e-05) | 5.487 (7e-05) | 0.0023 |
| wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis | Mean (std) | 5.508 (0.057) | 5.508 (0.020) | 5.503 (0.017) | 5.502 (0.017) | 0.0032 |
| wavelet.HHH_glcm_Imc1 | Mean (std) | 5.485 (1e-05) | 5.485 (1e-05) | 5.485 (2e-05) | 5.485 (1e-05) | 0.0145 |
| wavelet.HHL_glcm_ClusterProminence | Mean (std) | 5.700 (0.358) | 5.748 (0.248) | 5.692 (0.222) | 5.703 (0.254) | 0.0348 |
| wavelet.HLH_glcm_DifferenceAverage | Mean (std) | 5.487 (2e-04) | 5.487 (2e-04) | 5.487 (2e-04) | 5.487 (2e-04) | 0.0384 |
| wavelet.HHH_glszm_GrayLevelNonUniformity | Mean (std) | 5.505 (0.020) | 5.514 (0.047) | 5.501 (0.015) | 5.511 (0.044) | 0.0529 |
| wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 7.348 (0.914) | 7.444 (0.670) | 7.351 (0.642) | 7.546 (0.728) | 0.1077 |
| wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis | Mean (std) | 5.485 (5e-06) | 5.485 (5e-06) | 5.485 (3e-06) | 5.485 (3e-06) | 0.1230 |
| wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 6.827 (0.697) | 6.748 (0.523) | 6.769 (0.545) | 6.887 (0.565) | 0.1665 |
| wavelet.HHH_glszm_GrayLevelNonUniformityNormalized | Mean (std) | 5.487 (0.001) | 5.487 (4e-04) | 5.487 (4e-04) | 5.487 (5e-04) | 0.1696 |
| log.sigma.5.0.mm.3D_firstorder_90Percentile | Mean (std) | 5.451 (0.279) | 5.510 (0.372) | 5.500 (0.307) | 5.428 (0.578) | 0.1705 |
| wavelet.LHH_firstorder_Skewness | Mean (std) | 5.485 (0.001) | 5.485 (0.001) | 5.485 (0.001) | 5.485 (0.001) | 0.1760 |
| wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 5.485 (9e-05) | 5.485 (4e-05) | 5.485 (7e-05) | 5.485 (5e-05) | 0.2189 |
| wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis | Mean (std) | 5.485 (6e-06) | 5.485 (6e-06) | 5.485 (7e-06) | 5.485 (6e-06) | 0.2246 |
| log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 5.485 (9e-06) | 5.485 (1e-05) | 5.485 (8e-06) | 5.485 (1e-05) | 0.2504 |
| wavelet.LLL_glcm_ClusterProminence | Mean (std) | 16.607 (1.106) | 16.995 (1.184) | 16.928 (1.170) | 16.812 (1.337) | 0.2752 |
| wavelet.HHH_glrlm_GrayLevelVariance | Mean (std) | 5.486 (1e-05) | 5.486 (9e-06) | 5.486 (5e-06) | 5.486 (1e-05) | 0.2819 |
| wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis | Mean (std) | 15.969 (3.046) | 16.098 (2.874) | 15.832 (3.055) | 16.495 (2.887) | 0.2850 |
| wavelet.LHH_glcm_Imc1 | Mean (std) | 5.485 (3e-05) | 5.485 (3e-05) | 5.485 (5e-05) | 5.485 (4e-05) | 0.2863 |
| wavelet.LHH_glszm_GrayLevelNonUniformity | Mean (std) | 5.820 (0.299) | 5.894 (0.358) | 5.825 (0.312) | 5.848 (0.315) | 0.4377 |
| wavelet.HHH_firstorder_RootMeanSquared | Mean (std) | 7.124 (2e-05) | 7.124 (2e-05) | 7.124 (2e-05) | 7.124 (2e-05) | 0.5500 |
| log.sigma.5.0.mm.3D_firstorder_Kurtosis | Mean (std) | 5.500 (0.009) | 5.500 (0.007) | 5.500 (0.011) | 5.499 (0.006) | 0.5760 |
| wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 5.485 (3e-04) | 5.485 (2e-04) | 5.485 (3e-04) | 5.485 (3e-04) | 0.6286 |
| log.sigma.2.0.mm.3D_glcm_Autocorrelation | Mean (std) | 6.310 (0.402) | 6.348 (0.362) | 6.344 (0.396) | 6.362 (0.372) | 0.6306 |
| original_glcm_Imc1 | Mean (std) | 5.484 (2e-04) | 5.484 (2e-04) | 5.484 (2e-04) | 5.484 (2e-04) | 0.6505 |
| log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 7.361 (0.926) | 7.373 (0.770) | 7.352 (0.849) | 7.439 (0.809) | 0.7444 |
| log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis | Mean (std) | 7.441 (1.803) | 7.272 (1.560) | 7.350 (1.858) | 7.386 (1.569) | 0.8484 |
| original_shape_VoxelVolume | Mean (std) | 10.365 (1.582) | 10.450 (1.436) | 10.270 (1.576) | 10.472 (1.394) | 0.8789 |
| log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized | Mean (std) | 5.485 (1e-04) | 5.485 (1e-04) | 5.485 (1e-04) | 5.485 (1e-04) | 0.9716 |
| wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis | Mean (std) | 19.215 (2.854) | 19.309 (2.556) | 19.237 (2.865) | 19.349 (2.569) | 0.9930 |
table13 %>% save_kable('tables/table13.pdf')
plot_boxplot(log_filtered_radiomics %>% select(wavelet.LHL_gldm_DependenceVariance,
wavelet.HHH_glcm_ClusterProminence,
wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis,
wavelet.HHH_glcm_Imc1,
wavelet.HHL_glcm_ClusterProminence,
wavelet.HLH_glcm_DifferenceAverage) %>%
cbind(Histology = lung_data_no_out$Histology),
by = "Histology",
ncol = 3,
theme_config = list(strip.text.x = element_text(size = 13), text = element_text(size=15)))
ggsave("figures/Fig18.tiff",width = 100, height = 100, device="tiff", dpi=300, units = "mm", scale = 0.9)
radiomics_stage <- log_filtered_radiomics %>% cbind(Overall.Stage = lung_data_no_out$Overall.Stage)
crostab <- crosstable(num_digits = 3,radiomics_stage, c(colnames(radiomics_stage %>% dplyr::select(.,-Overall.Stage))), by = Overall.Stage, test = TRUE)
(table13.1 <- crostab %>% separate(test,
into = c('pvalue',
'test'),
sep = "\n") %>%
separate(pvalue,
into = c('string',
'pval'),
sep = ":",
convert = TRUE) %>%
arrange(pval) %>%
filter(variable == 'Mean (std)') %>%
dplyr::select(.,-c(string,.id)) %>% kable(full_with = FALSE) %>% kable_classic_2())
| label | variable | I | II | IIIa | IIIb | pval | test |
|---|---|---|---|---|---|---|---|
| wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis | Mean (std) | 15.346 (2.887) | 17.138 (2.686) | 15.755 (2.790) | 16.601 (2.985) | 0.0002 | (Kruskal-Wallis rank sum test) |
| log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 7.214 (0.928) | 7.616 (0.877) | 7.237 (0.718) | 7.516 (0.793) | 0.0004 | (Kruskal-Wallis rank sum test) |
| wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 6.716 (0.668) | 7.021 (0.620) | 6.721 (0.516) | 6.874 (0.535) | 0.0005 | (Kruskal-Wallis rank sum test) |
| wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis | Mean (std) | 5.485 (3e-06) | 5.485 (3e-06) | 5.485 (3e-06) | 5.485 (5e-06) | 0.0007 | (Kruskal-Wallis rank sum test) |
| original_shape_VoxelVolume | Mean (std) | 9.980 (1.427) | 10.766 (1.461) | 10.255 (1.403) | 10.616 (1.466) | 0.0016 | (Kruskal-Wallis rank sum test) |
| wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis | Mean (std) | 18.651 (2.498) | 19.903 (2.680) | 18.945 (2.615) | 19.649 (2.649) | 0.0017 | (Kruskal-Wallis rank sum test) |
| wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis | Mean (std) | 5.485 (5e-06) | 5.485 (5e-06) | 5.485 (6e-06) | 5.485 (6e-06) | 0.0032 | (Kruskal-Wallis rank sum test) |
| log.sigma.5.0.mm.3D_firstorder_Kurtosis | Mean (std) | 5.499 (0.007) | 5.500 (0.006) | 5.500 (0.010) | 5.500 (0.006) | 0.0036 | (Kruskal-Wallis rank sum test) |
| wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 7.369 (0.822) | 7.673 (0.884) | 7.326 (0.647) | 7.527 (0.685) | 0.0065 | (Kruskal-Wallis rank sum test) |
| log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis | Mean (std) | 6.903 (1.486) | 7.571 (1.650) | 7.297 (1.738) | 7.516 (1.615) | 0.0137 | (Kruskal-Wallis rank sum test) |
| log.sigma.5.0.mm.3D_firstorder_90Percentile | Mean (std) | 5.347 (0.423) | 5.537 (0.287) | 5.496 (0.351) | 5.480 (0.533) | 0.0196 | (Kruskal-Wallis rank sum test) |
| wavelet.LLL_glcm_ClusterProminence | Mean (std) | 17.136 (1.038) | 16.772 (1.281) | 17.000 (1.118) | 16.677 (1.347) | 0.0238 | (Kruskal-Wallis rank sum test) |
| wavelet.LHH_glszm_GrayLevelNonUniformity | Mean (std) | 5.808 (0.317) | 5.885 (0.291) | 5.829 (0.347) | 5.882 (0.322) | 0.0601 | (Kruskal-Wallis rank sum test) |
| wavelet.HHH_glszm_GrayLevelNonUniformityNormalized | Mean (std) | 5.487 (0.001) | 5.487 (5e-04) | 5.487 (5e-04) | 5.487 (5e-04) | 0.1096 | (Kruskal-Wallis rank sum test) |
| wavelet.HHH_glszm_GrayLevelNonUniformity | Mean (std) | 5.505 (0.036) | 5.513 (0.044) | 5.511 (0.046) | 5.510 (0.034) | 0.1205 | (Kruskal-Wallis rank sum test) |
| wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 5.485 (3e-04) | 5.485 (2e-04) | 5.485 (3e-04) | 5.485 (3e-04) | 0.1369 | (Kruskal-Wallis rank sum test) |
| wavelet.LHL_gldm_DependenceVariance | Mean (std) | 5.539 (0.026) | 5.550 (0.030) | 5.538 (0.025) | 5.544 (0.029) | 0.1398 | (Kruskal-Wallis rank sum test) |
| wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 5.485 (5e-05) | 5.485 (1e-04) | 5.485 (6e-05) | 5.485 (5e-05) | 0.1514 | (Kruskal-Wallis rank sum test) |
| wavelet.LHH_firstorder_Skewness | Mean (std) | 5.485 (0.001) | 5.485 (0.001) | 5.485 (0.001) | 5.485 (0.001) | 0.1915 | (Kruskal-Wallis rank sum test) |
| log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized | Mean (std) | 5.485 (1e-04) | 5.485 (1e-04) | 5.485 (1e-04) | 5.485 (1e-04) | 0.2239 | (One-way analysis of means) |
| wavelet.HHH_firstorder_RootMeanSquared | Mean (std) | 7.124 (2e-05) | 7.124 (2e-05) | 7.124 (1e-05) | 7.124 (2e-05) | 0.2252 | (Kruskal-Wallis rank sum test) |
| log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 5.485 (7e-06) | 5.485 (1e-05) | 5.485 (7e-06) | 5.485 (1e-05) | 0.2544 | (Kruskal-Wallis rank sum test) |
| log.sigma.2.0.mm.3D_glcm_Autocorrelation | Mean (std) | 6.342 (0.440) | 6.411 (0.389) | 6.309 (0.350) | 6.361 (0.363) | 0.2838 | (Kruskal-Wallis rank sum test) |
| wavelet.HHL_glcm_ClusterProminence | Mean (std) | 5.732 (0.229) | 5.750 (0.402) | 5.709 (0.253) | 5.703 (0.245) | 0.2934 | (Kruskal-Wallis rank sum test) |
| wavelet.LHH_glcm_Imc1 | Mean (std) | 5.485 (4e-05) | 5.485 (4e-05) | 5.485 (4e-05) | 5.485 (4e-05) | 0.2945 | (Kruskal-Wallis rank sum test) |
| wavelet.HHH_glcm_ClusterProminence | Mean (std) | 5.487 (7e-05) | 5.487 (2e-04) | 5.487 (9e-05) | 5.487 (6e-05) | 0.3604 | (Kruskal-Wallis rank sum test) |
| wavelet.HHH_glrlm_GrayLevelVariance | Mean (std) | 5.486 (7e-06) | 5.486 (1e-05) | 5.486 (1e-05) | 5.486 (6e-06) | 0.4089 | (Kruskal-Wallis rank sum test) |
| wavelet.HLH_glcm_DifferenceAverage | Mean (std) | 5.487 (2e-04) | 5.487 (2e-04) | 5.487 (2e-04) | 5.487 (2e-04) | 0.4570 | (Kruskal-Wallis rank sum test) |
| wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis | Mean (std) | 5.505 (0.017) | 5.512 (0.065) | 5.503 (0.016) | 5.504 (0.020) | 0.4824 | (Kruskal-Wallis rank sum test) |
| wavelet.HHH_glcm_Imc1 | Mean (std) | 5.485 (2e-05) | 5.485 (1e-05) | 5.485 (1e-05) | 5.485 (1e-05) | 0.7033 | (Kruskal-Wallis rank sum test) |
| original_glcm_Imc1 | Mean (std) | 5.484 (1e-04) | 5.484 (2e-04) | 5.484 (2e-04) | 5.484 (2e-04) | 0.8081 | (Kruskal-Wallis rank sum test) |
table13.1 %>% save_kable('tables/table13.1.pdf')
plot_boxplot(log_filtered_radiomics %>% select(wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis,
log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis,
wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis,
wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis,
original_shape_VoxelVolume,
wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis) %>%
cbind(Overall.Stage = lung_data_no_out$Overall.Stage),
by = "Overall.Stage",
ncol = 3,
theme_config = list(strip.text.x = element_text(size = 13), text = element_text(size=15)))
ggsave("figures/Fig18.1.tiff",width = 100, height = 100, device="tiff", dpi=300, units = "mm", scale = 0.9)
Now I will evaluate potential relationship between individual radiomic features and survival. To do so I adjust a cox model for each one.
radiomics_surv <- log_filtered_radiomics %>% cbind(Survival.time = lung_data_no_out$Survival.time, deadstatus.event = lung_data_no_out$deadstatus.event)
surv_formulas <- sapply(colnames(log_filtered_radiomics),function(x) as.formula(paste('Surv(Survival.time,as.numeric(deadstatus.event))~', x)))
surv_models <- lapply(surv_formulas, function(x){coxph(x, data = radiomics_surv)})
surv_rad_vars <- lapply(surv_models,
function(x){
x <- summary(x)
p.value<-format(signif(x$wald["pvalue"], digits=2),scientific = FALSE)
wald.test<-signif(x$wald["test"], digits=2)
beta<-signif(x$coef[1], digits=2);#coeficient beta
res<-c(beta, wald.test, p.value)
names(res)<-c("beta", "wald.test",
"p.value")
return(res)
#return(exp(cbind(coef(x),confint(x))))
})
res <- t(as.data.frame(surv_rad_vars, check.names = FALSE))
(table14 <- as.data.frame(res) %>% arrange(p.value) %>%
kable(full_with = FALSE) %>% kable_classic_2())
| beta | wald.test | p.value | |
|---|---|---|---|
| wavelet.HHH_glszm_GrayLevelNonUniformity | 5.2 | 19 | 0.000016 |
| log.sigma.5.0.mm.3D_firstorder_Kurtosis | 27 | 17 | 0.000043 |
| log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis | 0.29 | 17 | 0.000046 |
| wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis | 0.36 | 13 | 0.00024 |
| wavelet.LHH_glszm_GrayLevelNonUniformity | 0.62 | 13 | 0.00032 |
| log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis | 0.12 | 13 | 0.00035 |
| wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis | 0.27 | 11 | 0.00076 |
| wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis | 0.067 | 11 | 0.001 |
| original_shape_VoxelVolume | 0.13 | 10 | 0.0014 |
| log.sigma.2.0.mm.3D_glcm_Autocorrelation | 0.41 | 7.7 | 0.0054 |
| wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis | 0.055 | 6.2 | 0.013 |
| wavelet.LHL_gldm_DependenceVariance | 4.4 | 4.9 | 0.026 |
| wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis | -440 | 4 | 0.044 |
| wavelet.HHH_glcm_Imc1 | -7000 | 3.4 | 0.064 |
| wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis | -19000 | 3.2 | 0.073 |
| wavelet.HHH_glszm_GrayLevelNonUniformityNormalized | -190 | 2.8 | 0.096 |
| wavelet.HHH_glrlm_GrayLevelVariance | 9100 | 2.6 | 0.11 |
| wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis | -25000 | 2.1 | 0.14 |
| wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis | -1600 | 2 | 0.16 |
| wavelet.LHH_firstorder_Skewness | 90 | 1.8 | 0.18 |
| wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis | 2 | 1.6 | 0.21 |
| wavelet.LHH_glcm_Imc1 | 1800 | 1.1 | 0.29 |
| wavelet.HHL_glcm_ClusterProminence | 0.2 | 1 | 0.31 |
| log.sigma.5.0.mm.3D_firstorder_90Percentile | 0.13 | 1 | 0.32 |
| log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized | -280 | 0.35 | 0.55 |
| wavelet.HHH_firstorder_RootMeanSquared | 1800 | 0.36 | 0.55 |
| wavelet.LLL_glcm_ClusterProminence | -0.026 | 0.31 | 0.58 |
| wavelet.HHH_glcm_ClusterProminence | -280 | 0.21 | 0.65 |
| original_glcm_Imc1 | -130 | 0.15 | 0.69 |
| log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis | 910 | 0.03 | 0.87 |
| wavelet.HLH_glcm_DifferenceAverage | -24 | 0.01 | 0.93 |
table14 %>% save_kable('tables/table14.pdf')
I construct final datasets to use as imput to fit the model-based clustering. I exclude from the datasets the considered ‘target variables’ including mainly Histology and both components of survival estimates (Survival.time and deadstatus.event). I will also evaluate stage descriptors and scanner model distribution among different clusters once the model if generated.
lung_data_filtered_radiomics <- cbind(lung_data_no_out %>%
dplyr::select(age,
gender),
log_filtered_radiomics)
(table19 <- describe(lung_data_filtered_radiomics) %>% dplyr::select(described_variables,mean,sd,se_mean,IQR,skewness,kurtosis) %>%
kable(full_with = FALSE) %>% kable_classic_2())
| described_variables | mean | sd | se_mean | IQR | skewness | kurtosis |
|---|---|---|---|---|---|---|
| age | 68.087348 | 9.9962540 | 0.5148333 | 13.9987000 | -0.3072182 | -0.2297816 |
| original_shape_VoxelVolume | 10.418014 | 1.4592795 | 0.0751567 | 2.2564480 | -0.4374695 | -0.5534142 |
| wavelet.HHL_glcm_ClusterProminence | 5.714404 | 0.2640022 | 0.0135968 | 0.2498732 | 2.7786461 | 12.3225476 |
| log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis | 7.353114 | 1.6432615 | 0.0846323 | 2.3483661 | 0.9858591 | 0.5654748 |
| log.sigma.2.0.mm.3D_glcm_Autocorrelation | 6.347803 | 0.3760835 | 0.0193693 | 0.3610674 | 2.3153056 | 8.8514924 |
| log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis | 5.484805 | 0.0000107 | 0.0000006 | 0.0000036 | 6.2615524 | 50.2451503 |
| wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis | 16.196668 | 2.9322681 | 0.1510195 | 4.0622767 | -0.5484747 | -0.3205776 |
| wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis | 5.504839 | 0.0267962 | 0.0013801 | 0.0225736 | 8.4503877 | 115.9378509 |
| wavelet.HHH_glszm_GrayLevelNonUniformity | 5.509692 | 0.0393431 | 0.0020263 | 0.0181493 | 5.0057489 | 31.3398745 |
| wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis | 7.456766 | 0.7279690 | 0.0374923 | 0.9025123 | 0.9604226 | 1.5132056 |
| wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis | 5.484800 | 0.0000038 | 0.0000002 | 0.0000020 | 4.8891785 | 35.0596521 |
| wavelet.HHH_glcm_ClusterProminence | 5.486953 | 0.0001008 | 0.0000052 | 0.0000909 | 7.6866405 | 100.5859224 |
| wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis | 6.817760 | 0.5701551 | 0.0293645 | 0.7324665 | 1.1819527 | 3.1492263 |
| wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis | 19.300837 | 2.6443448 | 0.1361907 | 3.5656335 | -0.6022647 | -0.1985876 |
| wavelet.LLL_glcm_ClusterProminence | 16.858400 | 1.2373871 | 0.0637287 | 1.5032439 | -0.5711949 | 0.8381037 |
| wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis | 5.484863 | 0.0000609 | 0.0000031 | 0.0000564 | 3.4677774 | 22.4587047 |
| wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis | 5.484803 | 0.0000059 | 0.0000003 | 0.0000047 | 2.3361783 | 6.1841269 |
| wavelet.HHH_glrlm_GrayLevelVariance | 5.485837 | 0.0000090 | 0.0000005 | 0.0000032 | 4.8122790 | 29.9362933 |
| log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized | 5.485334 | 0.0001201 | 0.0000062 | 0.0001612 | 0.0031770 | 0.1886998 |
| wavelet.HHH_glcm_Imc1 | 5.484722 | 0.0000144 | 0.0000007 | 0.0000187 | -1.0817978 | 1.2107301 |
| original_glcm_Imc1 | 5.483915 | 0.0001680 | 0.0000087 | 0.0002174 | -0.4533090 | 1.5221494 |
| log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis | 7.394164 | 0.8180300 | 0.0421307 | 1.2165610 | 0.6620482 | 0.3129117 |
| wavelet.LHH_glszm_GrayLevelNonUniformity | 5.854420 | 0.3259955 | 0.0167896 | 0.4082331 | 1.4847299 | 2.6080324 |
| wavelet.LHH_firstorder_Skewness | 5.485200 | 0.0008812 | 0.0000454 | 0.0007537 | 1.0663667 | 8.8730064 |
| wavelet.HHH_glszm_GrayLevelNonUniformityNormalized | 5.486696 | 0.0004680 | 0.0000241 | 0.0008090 | -0.0653329 | -0.7465740 |
| wavelet.LHH_glcm_Imc1 | 5.484581 | 0.0000374 | 0.0000019 | 0.0000443 | -1.0972745 | 2.1929685 |
| log.sigma.5.0.mm.3D_firstorder_Kurtosis | 5.499964 | 0.0074873 | 0.0003856 | 0.0056462 | 3.5115187 | 20.3689620 |
| log.sigma.5.0.mm.3D_firstorder_90Percentile | 5.467547 | 0.4489193 | 0.0231205 | 0.2638859 | -5.4629759 | 56.1573713 |
| wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis | 5.485126 | 0.0002600 | 0.0000134 | 0.0001944 | 2.1247949 | 4.5987508 |
| wavelet.HLH_glcm_DifferenceAverage | 5.486741 | 0.0001939 | 0.0000100 | 0.0002108 | 1.7761862 | 5.4551338 |
| wavelet.HHH_firstorder_RootMeanSquared | 7.123670 | 0.0000189 | 0.0000010 | 0.0000110 | -3.6491017 | 19.5451207 |
| wavelet.LHL_gldm_DependenceVariance | 5.542137 | 0.0277151 | 0.0014274 | 0.0417894 | 0.5354665 | -0.3600384 |
table19 %>% save_kable('tables/table19.pdf')
As we can see value ranges differ significantly between numerical variables (radiomics and age included) this could bias the weight of each variable when contributing to the model so it is recomended to have all variables in the same scale. I chose to use min-max scaling. Then categorical variables will be directly treated by VarCellCM.
filtered_cont_scaled <- lung_data_filtered_radiomics %>% keep(is.numeric) %>%
lapply(function(x){(x - min(x)) / (max(x) - min(x))}) %>% as.data.frame()
lung_data_filtered_scaled_radiomics <- lung_data_filtered_radiomics %>%
keep(is.factor) %>%
cbind(filtered_cont_scaled)
(table20 <- describe(lung_data_filtered_scaled_radiomics) %>%
dplyr::select(described_variables,mean,sd,p00, p50, p100, skewness,kurtosis) %>% kable(full_with = FALSE) %>% kable_classic_2())
| described_variables | mean | sd | p00 | p50 | p100 | skewness | kurtosis |
|---|---|---|---|---|---|---|---|
| age | 0.5929473 | 0.1722916 | 0 | 0.5959248 | 1 | -0.3072182 | -0.2297816 |
| original_shape_VoxelVolume | 0.5829356 | 0.2252327 | 0 | 0.6175421 | 1 | -0.4374695 | -0.5534142 |
| wavelet.HHL_glcm_ClusterProminence | 0.0989860 | 0.1163366 | 0 | 0.0615282 | 1 | 2.7786461 | 12.3225476 |
| log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis | 0.2387529 | 0.2103486 | 0 | 0.2024770 | 1 | 0.9858591 | 0.5654748 |
| log.sigma.2.0.mm.3D_glcm_Autocorrelation | 0.2029748 | 0.1263963 | 0 | 0.1838236 | 1 | 2.3153056 | 8.8514924 |
| log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis | 0.0508914 | 0.0877837 | 0 | 0.0302539 | 1 | 6.2615524 | 50.2451503 |
| wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis | 0.5485125 | 0.2159559 | 0 | 0.5862543 | 1 | -0.5484747 | -0.3205776 |
| wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis | 0.0475191 | 0.0658836 | 0 | 0.0270760 | 1 | 8.4503877 | 115.9378509 |
| wavelet.HHH_glszm_GrayLevelNonUniformity | 0.0561877 | 0.1065138 | 0 | 0.0245006 | 1 | 5.0057489 | 31.3398745 |
| wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis | 0.3153470 | 0.1592984 | 0 | 0.2936679 | 1 | 0.9604226 | 1.5132056 |
| wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis | 0.0630528 | 0.0949388 | 0 | 0.0329395 | 1 | 4.8891785 | 35.0596521 |
| wavelet.HHH_glcm_ClusterProminence | 0.0755803 | 0.0663751 | 0 | 0.0659739 | 1 | 7.6866405 | 100.5859224 |
| wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis | 0.2740752 | 0.1561735 | 0 | 0.2616347 | 1 | 1.1819527 | 3.1492263 |
| wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis | 0.5865928 | 0.2011479 | 0 | 0.6240089 | 1 | -0.6022647 | -0.1985876 |
| wavelet.LLL_glcm_ClusterProminence | 0.5862742 | 0.1577118 | 0 | 0.6066546 | 1 | -0.5711949 | 0.8381037 |
| wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis | 0.0998352 | 0.0973916 | 0 | 0.0696657 | 1 | 3.4677774 | 22.4587047 |
| wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis | 0.1630327 | 0.1725658 | 0 | 0.0976298 | 1 | 2.3361783 | 6.1841269 |
| wavelet.HHH_glrlm_GrayLevelVariance | 0.0673688 | 0.1069052 | 0 | 0.0246964 | 1 | 4.8122790 | 29.9362933 |
| log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized | 0.4370991 | 0.1545526 | 0 | 0.4409033 | 1 | 0.0031770 | 0.1886998 |
| wavelet.HHH_glcm_Imc1 | 0.7284170 | 0.1749918 | 0 | 0.7690662 | 1 | -1.0817978 | 1.2107301 |
| original_glcm_Imc1 | 0.6018814 | 0.1204200 | 0 | 0.6094712 | 1 | -0.4533090 | 1.5221494 |
| log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis | 0.3233587 | 0.1855941 | 0 | 0.2940825 | 1 | 0.6620482 | 0.3129117 |
| wavelet.LHH_glszm_GrayLevelNonUniformity | 0.1880146 | 0.1677015 | 0 | 0.1479853 | 1 | 1.4847299 | 2.6080324 |
| wavelet.LHH_firstorder_Skewness | 0.4731001 | 0.0843611 | 0 | 0.4633876 | 1 | 1.0663667 | 8.8730064 |
| wavelet.HHH_glszm_GrayLevelNonUniformityNormalized | 0.4335451 | 0.2156077 | 0 | 0.5136202 | 1 | -0.0653329 | -0.7465740 |
| wavelet.LHH_glcm_Imc1 | 0.6823681 | 0.1463222 | 0 | 0.6952481 | 1 | -1.0972745 | 2.1929685 |
| log.sigma.5.0.mm.3D_firstorder_Kurtosis | 0.1050651 | 0.1035381 | 0 | 0.0753691 | 1 | 3.5115187 | 20.3689620 |
| log.sigma.5.0.mm.3D_firstorder_90Percentile | 0.8477223 | 0.0711705 | 0 | 0.8576667 | 1 | -5.4629759 | 56.1573713 |
| wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis | 0.2129439 | 0.1804696 | 0 | 0.1778410 | 1 | 2.1247949 | 4.5987508 |
| wavelet.HLH_glcm_DifferenceAverage | 0.2566230 | 0.1283222 | 0 | 0.2391226 | 1 | 1.7761862 | 5.4551338 |
| wavelet.HHH_firstorder_RootMeanSquared | 0.7815879 | 0.1032906 | 0 | 0.8015007 | 1 | -3.6491017 | 19.5451207 |
| wavelet.LHL_gldm_DependenceVariance | 0.3538225 | 0.2104678 | 0 | 0.3097211 | 1 | 0.5354665 | -0.3600384 |
table20 %>% save_kable('tables/table20.pdf')
First I will generate a model based clustering model using the MRMR filtered radiomics dataset with the added clinical variables and scaling done in previous points. I generate the model using VarSelCluster function from VarSelCM package, using variable selection. I display main discriminative variables given the model adjustment and clusters in 3D scatter plot using principal components as main axes for the representation. I also display the uncertainty probability for each cluster.
# I adjust VarSelCluster model with variables selection for this first
# approach and using BIC criteria to estimate the optimal number of clusters
set.seed(12345)
res_varsel_select <- VarSelCluster(x = lung_data_filtered_scaled_radiomics,
gvals = 2:10,
nbcores = 4,
vbleSelec = TRUE,
crit.varsel = "BIC")
# I check the resulting model summary
summary(res_varsel_select)
## Model:
## Number of components: 10
## Model selection has been performed according to the BIC criterion
## Variable selection has been performed, 28 ( 84.85 % ) of the variables are relevant for clustering
##
res_varsel_select@model@names.relevant
## [1] "original_shape_VoxelVolume"
## [2] "wavelet.HHL_glcm_ClusterProminence"
## [3] "log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis"
## [4] "log.sigma.2.0.mm.3D_glcm_Autocorrelation"
## [5] "log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis"
## [6] "wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis"
## [7] "wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis"
## [8] "wavelet.HHH_glszm_GrayLevelNonUniformity"
## [9] "wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis"
## [10] "wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis"
## [11] "wavelet.HHH_glcm_ClusterProminence"
## [12] "wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis"
## [13] "wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis"
## [14] "wavelet.LLL_glcm_ClusterProminence"
## [15] "wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis"
## [16] "wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis"
## [17] "wavelet.HHH_glrlm_GrayLevelVariance"
## [18] "log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis"
## [19] "wavelet.LHH_glszm_GrayLevelNonUniformity"
## [20] "wavelet.LHH_firstorder_Skewness"
## [21] "wavelet.HHH_glszm_GrayLevelNonUniformityNormalized"
## [22] "wavelet.LHH_glcm_Imc1"
## [23] "log.sigma.5.0.mm.3D_firstorder_Kurtosis"
## [24] "log.sigma.5.0.mm.3D_firstorder_90Percentile"
## [25] "wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis"
## [26] "wavelet.HLH_glcm_DifferenceAverage"
## [27] "wavelet.HHH_firstorder_RootMeanSquared"
## [28] "wavelet.LHL_gldm_DependenceVariance"
res_varsel_select@model@names.irrelevant
## [1] "age"
## [2] "log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized"
## [3] "wavelet.HHH_glcm_Imc1"
## [4] "original_glcm_Imc1"
## [5] "gender"
# I check the discriminative power of the variables
tiff(filename = "figures/Fig22.tiff", width = 250, height = 150, units = "mm", res = 300)
plot(res_varsel_select)
fig <- dev.off()
plot(res_varsel_select)
# I check fit for main discriminative variables
tiff(filename = "figures/Fig22.1.tiff", width = 200, height = 800, units = "mm", res = 300)
plot(res_varsel_select, y="original_shape_VoxelVolume", type="cdf")
plot(res_varsel_select, y="wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis", type="cdf")
plot(res_varsel_select, y="wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis", type="cdf")
fig <- dev.off()
plot(res_varsel_select, y="original_shape_VoxelVolume", type="cdf")
plot(res_varsel_select, y="wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis", type="cdf")
plot(res_varsel_select, y="wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis", type="cdf")
# I build q data frame including partitions, variables used in the model and other variables of interest
lung_data_res_varsel <- lung_data_filtered_scaled_radiomics %>%
cbind(lung_data_no_out %>% dplyr::select(Histology,
Survival.time,
deadstatus.event,
Manufacturer,
Overall.Stage,
clinical.T.Stage,
Clinical.N.Stage),
partition = as.factor(res_varsel_select@partitions@zMAP))
# I build a data frame with principal components for radiomic variables to ease visualisation of results
pc_radiomics <- prcomp(lung_data_filtered_scaled_radiomics %>% keep(is.numeric), scale = TRUE)
pc_rad <- pc_radiomics$x[,1:3]
pc_df <- as.data.frame(pc_rad %>% cbind(partition = as.factor(res_varsel_select@partitions@zMAP)))
# I generate a representation of the clusters using the main discriminative variables to define the axes
# (screencapture saved as figure 23)
plot_ly(pc_df, x=~PC1, y=~PC2,
z=~PC3, color=~as.factor(partition)) %>%
add_markers(size=1.5)%>%
layout(
scene = list(
xaxis = list(size = 0.5),
yaxis = list(size = 0.5),
zaxis = list(size = 0.5)))
# I calculate a distance metric to be able to measure Silhouette Coefficient:
gower_dist <- daisy(lung_data_filtered_scaled_radiomics,
metric = "gower")
sil <- silhouette(res_varsel_select@partitions@zMAP, gower_dist)
summary(sil)
## Silhouette of 377 units in 10 clusters from silhouette.default(x = res_varsel_select@partitions@zMAP, dist = gower_dist) :
## Cluster sizes and average silhouette widths:
## 36 50 53 37 13 64
## 0.142042280 0.135047294 0.090876392 0.094163903 0.037173773 0.108424559
## 20 30 33 41
## -0.038858670 0.008651534 0.125917927 0.014436926
## Individual silhouette widths:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.233547 0.008689 0.089835 0.084399 0.163296 0.310344
tiff(filename = "figures/Fig23.1.tiff", width = 200, height = 150, units = "mm", res = 300)
plot(sil)
fig <- dev.off()
plot(sil)
# I display a summary of the probabilities of missclassification for each cluster
tiff(filename = "figures/Fig24.tiff", width = 200, height = 300, units = "mm", res = 300)
plot(res_varsel_select, type="probs-class")
fig <- dev.off()
plot(res_varsel_select, type="probs-class")
(crosstab<- crosstable(lung_data_res_varsel %>% dplyr::select(.,-c(deadstatus.event,Survival.time)), showNA = FALSE,
by = partition) %>% flextable()%>% autofit())
.id | label | variable | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
gender | gender | female | 12 (10.17%) | 13 (11.02%) | 12 (10.17%) | 20 (16.95%) | 3 (2.54%) | 22 (18.64%) | 7 (5.93%) | 7 (5.93%) | 9 (7.63%) | 13 (11.02%) |
gender | gender | male | 24 (9.27%) | 37 (14.29%) | 41 (15.83%) | 17 (6.56%) | 10 (3.86%) | 42 (16.22%) | 13 (5.02%) | 23 (8.88%) | 24 (9.27%) | 28 (10.81%) |
age | age | Min / Max | 0 / 0.9 | 0.2 / 0.9 | 0.2 / 1.0 | 0.3 / 0.8 | 0.3 / 0.8 | 0.2 / 0.9 | 0.3 / 0.8 | 0.3 / 0.9 | 0.2 / 0.9 | 0.2 / 0.9 |
age | age | Med [IQR] | 0.6 [0.5;0.7] | 0.5 [0.4;0.7] | 0.6 [0.5;0.7] | 0.7 [0.5;0.7] | 0.6 [0.5;0.8] | 0.6 [0.5;0.7] | 0.6 [0.5;0.7] | 0.6 [0.4;0.7] | 0.7 [0.5;0.8] | 0.6 [0.5;0.7] |
age | age | Mean (std) | 0.6 (0.2) | 0.6 (0.2) | 0.6 (0.2) | 0.6 (0.2) | 0.6 (0.2) | 0.6 (0.2) | 0.6 (0.2) | 0.6 (0.2) | 0.6 (0.2) | 0.6 (0.1) |
age | age | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
original_shape_VoxelVolume | original_shape_VoxelVolume | Min / Max | 0 / 0.3 | 0.6 / 1.0 | 0.4 / 0.9 | 0.3 / 0.7 | 0.6 / 0.9 | 0.3 / 0.7 | 0.1 / 0.5 | 0.2 / 1.0 | 0.7 / 1.0 | 0.5 / 0.9 |
original_shape_VoxelVolume | original_shape_VoxelVolume | Med [IQR] | 0.2 [0.1;0.3] | 0.8 [0.8;0.9] | 0.7 [0.6;0.7] | 0.4 [0.4;0.5] | 0.7 [0.7;0.8] | 0.5 [0.4;0.6] | 0.3 [0.2;0.3] | 0.6 [0.6;0.7] | 0.8 [0.8;0.9] | 0.7 [0.6;0.8] |
original_shape_VoxelVolume | original_shape_VoxelVolume | Mean (std) | 0.2 (0.1) | 0.8 (0.1) | 0.7 (0.1) | 0.4 (0.1) | 0.8 (0.1) | 0.5 (0.1) | 0.3 (0.1) | 0.6 (0.1) | 0.8 (0.1) | 0.7 (0.1) |
original_shape_VoxelVolume | original_shape_VoxelVolume | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
wavelet.HHL_glcm_ClusterProminence | wavelet.HHL_glcm_ClusterProminence | Min / Max | 0.001 / 0.2 | 0.002 / 0.1 | 0.01 / 0.2 | 0.01 / 0.4 | 0.02 / 1.0 | 0.002 / 0.1 | 0.1 / 0.7 | 0 / 0.1 | 0.02 / 0.2 | 0.1 / 0.4 |
wavelet.HHL_glcm_ClusterProminence | wavelet.HHL_glcm_ClusterProminence | Med [IQR] | 0.1 [0.03;0.1] | 0.01 [0.008;0.02] | 0.1 [0.1;0.1] | 0.1 [0.1;0.2] | 0.2 [0.1;0.3] | 0.03 [0.01;0.04] | 0.4 [0.2;0.4] | 0.02 [0.008;0.04] | 0.1 [0.1;0.1] | 0.2 [0.2;0.2] |
wavelet.HHL_glcm_ClusterProminence | wavelet.HHL_glcm_ClusterProminence | Mean (std) | 0.1 (0.04) | 0.02 (0.01) | 0.1 (0.05) | 0.1 (0.1) | 0.3 (0.3) | 0.03 (0.02) | 0.3 (0.2) | 0.03 (0.03) | 0.1 (0.04) | 0.2 (0.1) |
wavelet.HHL_glcm_ClusterProminence | wavelet.HHL_glcm_ClusterProminence | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis | log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis | Min / Max | 8e-05 / 0.1 | 0.1 / 0.9 | 0.03 / 0.6 | 0.001 / 0.5 | 0.1 / 0.6 | 3e-04 / 0.3 | 0 / 0.3 | 0.1 / 1.0 | 0.1 / 0.7 | 0.04 / 0.8 |
log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis | log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis | Med [IQR] | 0.006 [0.002;0.02] | 0.5 [0.3;0.6] | 0.2 [0.2;0.3] | 0.1 [0.02;0.1] | 0.3 [0.2;0.5] | 0.1 [0.1;0.2] | 0.01 [0.003;0.04] | 0.3 [0.3;0.5] | 0.4 [0.3;0.5] | 0.2 [0.1;0.4] |
log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis | log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis | Mean (std) | 0.01 (0.02) | 0.5 (0.2) | 0.3 (0.1) | 0.1 (0.1) | 0.3 (0.2) | 0.1 (0.1) | 0.03 (0.1) | 0.4 (0.2) | 0.4 (0.1) | 0.3 (0.2) |
log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis | log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
log.sigma.2.0.mm.3D_glcm_Autocorrelation | log.sigma.2.0.mm.3D_glcm_Autocorrelation | Min / Max | 0.01 / 0.3 | 0.1 / 0.3 | 0.1 / 0.4 | 0.04 / 0.3 | 0.3 / 1.0 | 0.03 / 0.4 | 0.1 / 0.5 | 0 / 0.2 | 0.2 / 0.7 | 0.1 / 0.5 |
log.sigma.2.0.mm.3D_glcm_Autocorrelation | log.sigma.2.0.mm.3D_glcm_Autocorrelation | Med [IQR] | 0.1 [0.1;0.2] | 0.2 [0.1;0.2] | 0.2 [0.1;0.2] | 0.2 [0.1;0.2] | 0.6 [0.5;0.8] | 0.1 [0.1;0.2] | 0.2 [0.1;0.3] | 0.1 [0.1;0.1] | 0.2 [0.2;0.3] | 0.3 [0.2;0.3] |
log.sigma.2.0.mm.3D_glcm_Autocorrelation | log.sigma.2.0.mm.3D_glcm_Autocorrelation | Mean (std) | 0.1 (0.1) | 0.2 (0.1) | 0.2 (0.1) | 0.2 (0.1) | 0.6 (0.2) | 0.2 (0.1) | 0.2 (0.1) | 0.1 (0.1) | 0.3 (0.1) | 0.3 (0.1) |
log.sigma.2.0.mm.3D_glcm_Autocorrelation | log.sigma.2.0.mm.3D_glcm_Autocorrelation | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis | log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis | Min / Max | 0.02 / 1.0 | 0.002 / 0.1 | 0.004 / 0.1 | 0.003 / 0.5 | 0 / 0.1 | 0.009 / 0.1 | 0.02 / 0.1 | 0.007 / 0.4 | 0.005 / 0.2 | 0.006 / 0.1 |
log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis | log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis | Med [IQR] | 0.05 [0.03;0.1] | 0.03 [0.02;0.04] | 0.03 [0.02;0.04] | 0.02 [0.02;0.04] | 0.03 [0.01;0.04] | 0.03 [0.02;0.04] | 0.05 [0.03;0.1] | 0.1 [0.03;0.1] | 0.02 [0.02;0.03] | 0.02 [0.01;0.03] |
log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis | log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 0.1 (0.2) | 0.03 (0.02) | 0.04 (0.02) | 0.1 (0.1) | 0.03 (0.02) | 0.03 (0.02) | 0.1 (0.03) | 0.1 (0.1) | 0.03 (0.03) | 0.03 (0.02) |
log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis | log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis | wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis | Min / Max | 0 / 0.4 | 0.6 / 1.0 | 0.5 / 0.7 | 0.2 / 0.7 | 0.7 / 0.9 | 0.2 / 0.7 | 0.1 / 0.6 | 0.2 / 0.8 | 0.7 / 0.9 | 0.5 / 0.8 |
wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis | wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis | Med [IQR] | 0.2 [0.1;0.3] | 0.8 [0.7;0.8] | 0.6 [0.6;0.7] | 0.4 [0.3;0.4] | 0.8 [0.8;0.9] | 0.5 [0.4;0.6] | 0.2 [0.1;0.3] | 0.6 [0.5;0.7] | 0.8 [0.7;0.8] | 0.6 [0.5;0.7] |
wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis | wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis | Mean (std) | 0.2 (0.1) | 0.8 (0.1) | 0.6 (0.1) | 0.4 (0.1) | 0.8 (0.1) | 0.5 (0.1) | 0.2 (0.2) | 0.6 (0.1) | 0.8 (0.1) | 0.6 (0.1) |
wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis | wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis | wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis | Min / Max | 0 / 0.05 | 0.002 / 0.03 | 0.009 / 0.1 | 0.003 / 0.1 | 0.02 / 1.0 | 0.002 / 0.03 | 0.02 / 0.2 | 0.001 / 0.1 | 0.01 / 0.1 | 0.1 / 0.2 |
wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis | wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis | Med [IQR] | 0.02 [0.01;0.03] | 0.01 [0.007;0.02] | 0.05 [0.03;0.1] | 0.05 [0.04;0.1] | 0.1 [0.1;0.1] | 0.009 [0.007;0.01] | 0.1 [0.1;0.2] | 0.02 [0.007;0.02] | 0.1 [0.04;0.1] | 0.1 [0.1;0.1] |
wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis | wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis | Mean (std) | 0.02 (0.01) | 0.01 (0.006) | 0.05 (0.02) | 0.1 (0.03) | 0.2 (0.3) | 0.01 (0.006) | 0.1 (0.1) | 0.02 (0.01) | 0.1 (0.03) | 0.1 (0.04) |
wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis | wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
wavelet.HHH_glszm_GrayLevelNonUniformity | wavelet.HHH_glszm_GrayLevelNonUniformity | Min / Max | 0 / 0.03 | 0.007 / 0.1 | 0 / 0.1 | 0 / 0.1 | 0.03 / 0.6 | 0 / 0.1 | 0 / 0.2 | 0 / 0.1 | 0.02 / 0.3 | 0.02 / 1.0 |
wavelet.HHH_glszm_GrayLevelNonUniformity | wavelet.HHH_glszm_GrayLevelNonUniformity | Med [IQR] | 0.007 [0;0.01] | 0.04 [0.03;0.1] | 0.02 [0.01;0.03] | 0.007 [0.004;0.01] | 0.1 [0.1;0.2] | 0.02 [0.007;0.03] | 0.02 [0.007;0.04] | 0.02 [0.007;0.03] | 0.1 [0.1;0.1] | 0.2 [0.1;0.2] |
wavelet.HHH_glszm_GrayLevelNonUniformity | wavelet.HHH_glszm_GrayLevelNonUniformity | Mean (std) | 0.008 (0.008) | 0.04 (0.03) | 0.03 (0.02) | 0.01 (0.01) | 0.2 (0.2) | 0.02 (0.01) | 0.03 (0.04) | 0.02 (0.01) | 0.1 (0.1) | 0.2 (0.2) |
wavelet.HHH_glszm_GrayLevelNonUniformity | wavelet.HHH_glszm_GrayLevelNonUniformity | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis | wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis | Min / Max | 0 / 0.2 | 0.2 / 0.6 | 0.2 / 0.7 | 0.1 / 0.4 | 0.6 / 1.0 | 0.1 / 0.6 | 0.1 / 0.7 | 0.1 / 0.6 | 0.2 / 0.7 | 0.2 / 0.6 |
wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis | wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis | Med [IQR] | 0.1 [0.1;0.1] | 0.4 [0.3;0.5] | 0.3 [0.3;0.4] | 0.2 [0.2;0.3] | 0.7 [0.7;0.8] | 0.2 [0.2;0.3] | 0.2 [0.2;0.3] | 0.2 [0.2;0.3] | 0.5 [0.4;0.5] | 0.4 [0.3;0.5] |
wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis | wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 0.1 (0.1) | 0.4 (0.1) | 0.3 (0.1) | 0.2 (0.1) | 0.8 (0.1) | 0.2 (0.1) | 0.3 (0.1) | 0.2 (0.1) | 0.5 (0.1) | 0.4 (0.1) |
wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis | wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis | wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis | Min / Max | 0.1 / 1.0 | 0.002 / 0.1 | 0.003 / 0.1 | 0.01 / 0.2 | 3e-04 / 0.009 | 0.01 / 0.2 | 0.02 / 0.4 | 0.01 / 0.1 | 0 / 0.02 | 0.008 / 0.1 |
wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis | wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis | Med [IQR] | 0.2 [0.1;0.3] | 0.01 [0.007;0.02] | 0.03 [0.02;0.03] | 0.1 [0.04;0.1] | 0.003 [8e-04;0.004] | 0.1 [0.04;0.1] | 0.1 [0.1;0.1] | 0.04 [0.02;0.1] | 0.01 [0.006;0.01] | 0.02 [0.02;0.03] |
wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis | wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis | Mean (std) | 0.3 (0.2) | 0.02 (0.01) | 0.03 (0.01) | 0.1 (0.04) | 0.003 (0.003) | 0.1 (0.04) | 0.1 (0.1) | 0.04 (0.03) | 0.01 (0.006) | 0.02 (0.01) |
wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis | wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
wavelet.HHH_glcm_ClusterProminence | wavelet.HHH_glcm_ClusterProminence | Min / Max | 0.04 / 0.2 | 0.005 / 0.1 | 0.01 / 0.1 | 0.03 / 0.2 | 0.01 / 1.0 | 0.008 / 0.2 | 0.1 / 0.4 | 0 / 0.1 | 0.006 / 0.1 | 0.003 / 0.2 |
wavelet.HHH_glcm_ClusterProminence | wavelet.HHH_glcm_ClusterProminence | Med [IQR] | 0.1 [0.1;0.1] | 0.1 [0.05;0.1] | 0.03 [0.02;0.1] | 0.1 [0.05;0.1] | 0.1 [0.1;0.2] | 0.1 [0.1;0.1] | 0.1 [0.1;0.2] | 0.05 [0.02;0.1] | 0.03 [0.02;0.1] | 0.1 [0.04;0.1] |
wavelet.HHH_glcm_ClusterProminence | wavelet.HHH_glcm_ClusterProminence | Mean (std) | 0.1 (0.03) | 0.1 (0.03) | 0.04 (0.02) | 0.1 (0.03) | 0.2 (0.3) | 0.1 (0.03) | 0.1 (0.1) | 0.04 (0.02) | 0.04 (0.03) | 0.1 (0.04) |
wavelet.HHH_glcm_ClusterProminence | wavelet.HHH_glcm_ClusterProminence | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis | wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis | Min / Max | 0 / 0.2 | 0.2 / 0.7 | 0.2 / 0.5 | 0.1 / 0.3 | 0.6 / 1.0 | 0.1 / 0.4 | 0.04 / 0.4 | 0.03 / 0.3 | 0.2 / 0.6 | 0.2 / 0.5 |
wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis | wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis | Med [IQR] | 0.1 [0.04;0.1] | 0.4 [0.3;0.4] | 0.3 [0.2;0.4] | 0.2 [0.1;0.2] | 0.7 [0.6;0.9] | 0.2 [0.1;0.3] | 0.1 [0.1;0.2] | 0.3 [0.2;0.3] | 0.4 [0.4;0.4] | 0.3 [0.3;0.4] |
wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis | wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 0.1 (0.04) | 0.4 (0.1) | 0.3 (0.1) | 0.2 (0.1) | 0.8 (0.2) | 0.2 (0.1) | 0.2 (0.1) | 0.2 (0.1) | 0.4 (0.1) | 0.3 (0.1) |
wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis | wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis | wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis | Min / Max | 0 / 0.4 | 0.6 / 1.0 | 0.5 / 0.9 | 0.2 / 0.7 | 0.6 / 0.9 | 0.3 / 0.7 | 0.1 / 0.4 | 0.3 / 0.9 | 0.7 / 0.9 | 0.5 / 0.8 |
wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis | wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis | Med [IQR] | 0.3 [0.2;0.3] | 0.8 [0.7;0.8] | 0.7 [0.6;0.7] | 0.5 [0.4;0.5] | 0.7 [0.7;0.8] | 0.5 [0.4;0.6] | 0.3 [0.2;0.4] | 0.7 [0.6;0.7] | 0.8 [0.8;0.8] | 0.6 [0.6;0.7] |
wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis | wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis | Mean (std) | 0.2 (0.1) | 0.8 (0.1) | 0.7 (0.1) | 0.5 (0.1) | 0.7 (0.1) | 0.5 (0.1) | 0.3 (0.1) | 0.7 (0.1) | 0.8 (0.1) | 0.6 (0.1) |
wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis | wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
wavelet.LLL_glcm_ClusterProminence | wavelet.LLL_glcm_ClusterProminence | Min / Max | 0.3 / 0.9 | 0.1 / 0.8 | 0.2 / 0.8 | 0.4 / 0.8 | 0.2 / 0.8 | 0.2 / 0.8 | 0.3 / 1.0 | 0 / 0.7 | 0.5 / 1.0 | 0.3 / 0.9 |
wavelet.LLL_glcm_ClusterProminence | wavelet.LLL_glcm_ClusterProminence | Med [IQR] | 0.6 [0.5;0.7] | 0.5 [0.4;0.6] | 0.6 [0.5;0.7] | 0.6 [0.6;0.7] | 0.7 [0.6;0.7] | 0.6 [0.5;0.7] | 0.7 [0.6;0.8] | 0.5 [0.3;0.5] | 0.6 [0.5;0.7] | 0.7 [0.6;0.8] |
wavelet.LLL_glcm_ClusterProminence | wavelet.LLL_glcm_ClusterProminence | Mean (std) | 0.6 (0.1) | 0.5 (0.2) | 0.6 (0.1) | 0.6 (0.1) | 0.6 (0.1) | 0.6 (0.1) | 0.7 (0.2) | 0.4 (0.2) | 0.7 (0.1) | 0.7 (0.1) |
wavelet.LLL_glcm_ClusterProminence | wavelet.LLL_glcm_ClusterProminence | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis | wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis | Min / Max | 0.1 / 1.0 | 0.03 / 0.2 | 0.02 / 0.1 | 0.03 / 0.2 | 0 / 0.04 | 0.1 / 0.3 | 0.02 / 0.2 | 0.04 / 0.3 | 0.008 / 0.1 | 0.01 / 0.1 |
wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis | wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis | Med [IQR] | 0.3 [0.2;0.3] | 0.1 [0.05;0.1] | 0.05 [0.04;0.1] | 0.1 [0.1;0.1] | 0.01 [0.007;0.02] | 0.1 [0.1;0.2] | 0.1 [0.1;0.1] | 0.1 [0.1;0.2] | 0.03 [0.02;0.04] | 0.03 [0.03;0.04] |
wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis | wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 0.3 (0.2) | 0.1 (0.04) | 0.1 (0.02) | 0.1 (0.03) | 0.02 (0.01) | 0.2 (0.1) | 0.1 (0.1) | 0.1 (0.1) | 0.03 (0.02) | 0.03 (0.01) |
wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis | wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis | wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis | Min / Max | 0.2 / 1.0 | 0.01 / 0.1 | 0.03 / 0.2 | 0.1 / 0.4 | 0 / 0.05 | 0.04 / 0.4 | 0.05 / 0.8 | 0.03 / 0.3 | 0.01 / 0.1 | 0.03 / 0.2 |
wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis | wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis | Med [IQR] | 0.5 [0.4;0.7] | 0.1 [0.03;0.1] | 0.1 [0.1;0.1] | 0.2 [0.1;0.2] | 0.02 [0.004;0.04] | 0.2 [0.1;0.2] | 0.3 [0.2;0.4] | 0.1 [0.1;0.2] | 0.04 [0.03;0.1] | 0.1 [0.1;0.1] |
wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis | wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis | Mean (std) | 0.5 (0.2) | 0.1 (0.02) | 0.1 (0.04) | 0.2 (0.1) | 0.02 (0.02) | 0.2 (0.1) | 0.3 (0.2) | 0.2 (0.1) | 0.04 (0.02) | 0.1 (0.03) |
wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis | wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
wavelet.HHH_glrlm_GrayLevelVariance | wavelet.HHH_glrlm_GrayLevelVariance | Min / Max | 0 / 0.02 | 0.02 / 0.03 | 0.02 / 0.1 | 0.02 / 0.2 | 0.05 / 0.9 | 0.02 / 0.02 | 0.01 / 1.0 | 0.02 / 0.02 | 0.02 / 0.1 | 0.04 / 0.5 |
wavelet.HHH_glrlm_GrayLevelVariance | wavelet.HHH_glrlm_GrayLevelVariance | Med [IQR] | 0.02 [0.02;0.02] | 0.02 [0.02;0.02] | 0.03 [0.03;0.05] | 0.05 [0.02;0.1] | 0.2 [0.1;0.3] | 0.02 [0.02;0.02] | 0.2 [0.1;0.4] | 0.02 [0.02;0.02] | 0.1 [0.04;0.1] | 0.1 [0.1;0.2] |
wavelet.HHH_glrlm_GrayLevelVariance | wavelet.HHH_glrlm_GrayLevelVariance | Mean (std) | 0.02 (0.004) | 0.02 (4e-04) | 0.04 (0.01) | 0.1 (0.05) | 0.2 (0.2) | 0.02 (1e-04) | 0.3 (0.3) | 0.02 (1e-04) | 0.1 (0.02) | 0.2 (0.1) |
wavelet.HHH_glrlm_GrayLevelVariance | wavelet.HHH_glrlm_GrayLevelVariance | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized | log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized | Min / Max | 0.1 / 0.9 | 0.1 / 0.8 | 0.1 / 0.8 | 0.05 / 1.0 | 0 / 0.7 | 0.1 / 0.7 | 0.2 / 0.8 | 0.02 / 0.6 | 0.1 / 0.7 | 0.1 / 0.7 |
log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized | log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized | Med [IQR] | 0.5 [0.3;0.6] | 0.5 [0.4;0.5] | 0.4 [0.3;0.5] | 0.5 [0.4;0.5] | 0.5 [0.3;0.6] | 0.4 [0.4;0.5] | 0.5 [0.3;0.7] | 0.4 [0.2;0.5] | 0.5 [0.4;0.5] | 0.4 [0.3;0.5] |
log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized | log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized | Mean (std) | 0.5 (0.2) | 0.5 (0.1) | 0.4 (0.1) | 0.5 (0.2) | 0.4 (0.2) | 0.4 (0.1) | 0.5 (0.2) | 0.3 (0.1) | 0.4 (0.2) | 0.4 (0.2) |
log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized | log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
wavelet.HHH_glcm_Imc1 | wavelet.HHH_glcm_Imc1 | Min / Max | 0.1 / 1.0 | 0.4 / 0.9 | 0.2 / 0.9 | 0.2 / 1.0 | 0.3 / 0.9 | 0.3 / 1.0 | 0 / 1.0 | 0.3 / 1.0 | 0.1 / 0.9 | 0.4 / 0.9 |
wavelet.HHH_glcm_Imc1 | wavelet.HHH_glcm_Imc1 | Med [IQR] | 0.8 [0.7;0.9] | 0.9 [0.7;0.9] | 0.7 [0.5;0.8] | 0.8 [0.7;0.9] | 0.7 [0.6;0.8] | 0.8 [0.8;0.9] | 0.8 [0.7;0.8] | 0.7 [0.5;0.8] | 0.6 [0.5;0.7] | 0.7 [0.6;0.8] |
wavelet.HHH_glcm_Imc1 | wavelet.HHH_glcm_Imc1 | Mean (std) | 0.7 (0.2) | 0.8 (0.1) | 0.7 (0.2) | 0.8 (0.2) | 0.7 (0.2) | 0.8 (0.1) | 0.7 (0.2) | 0.7 (0.2) | 0.6 (0.2) | 0.7 (0.1) |
wavelet.HHH_glcm_Imc1 | wavelet.HHH_glcm_Imc1 | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
original_glcm_Imc1 | original_glcm_Imc1 | Min / Max | 0 / 0.7 | 0.3 / 0.8 | 0.4 / 0.9 | 0.4 / 0.8 | 0.4 / 0.8 | 0.3 / 0.7 | 0.3 / 0.8 | 0.5 / 0.9 | 0.3 / 0.8 | 0.3 / 1.0 |
original_glcm_Imc1 | original_glcm_Imc1 | Med [IQR] | 0.6 [0.5;0.7] | 0.6 [0.5;0.7] | 0.6 [0.5;0.7] | 0.7 [0.6;0.7] | 0.5 [0.5;0.6] | 0.6 [0.5;0.7] | 0.6 [0.5;0.7] | 0.6 [0.6;0.7] | 0.6 [0.5;0.7] | 0.6 [0.6;0.7] |
original_glcm_Imc1 | original_glcm_Imc1 | Mean (std) | 0.6 (0.2) | 0.6 (0.1) | 0.6 (0.1) | 0.7 (0.1) | 0.6 (0.1) | 0.6 (0.1) | 0.6 (0.1) | 0.7 (0.1) | 0.6 (0.1) | 0.6 (0.1) |
original_glcm_Imc1 | original_glcm_Imc1 | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis | log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis | Min / Max | 0 / 0.3 | 0.3 / 0.7 | 0.2 / 0.6 | 0.04 / 0.3 | 0.6 / 1.0 | 0.04 / 0.5 | 0.05 / 0.4 | 0.1 / 0.5 | 0.4 / 0.8 | 0.2 / 0.6 |
log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis | log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis | Med [IQR] | 0.1 [0.1;0.1] | 0.5 [0.4;0.5] | 0.3 [0.3;0.4] | 0.2 [0.1;0.2] | 0.8 [0.7;0.8] | 0.2 [0.2;0.3] | 0.2 [0.1;0.2] | 0.3 [0.2;0.4] | 0.5 [0.5;0.6] | 0.4 [0.3;0.4] |
log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis | log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 0.1 (0.1) | 0.5 (0.1) | 0.3 (0.1) | 0.2 (0.1) | 0.8 (0.1) | 0.2 (0.1) | 0.2 (0.1) | 0.3 (0.1) | 0.5 (0.1) | 0.4 (0.1) |
log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis | log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
wavelet.LHH_glszm_GrayLevelNonUniformity | wavelet.LHH_glszm_GrayLevelNonUniformity | Min / Max | 0 / 0.1 | 0.04 / 0.5 | 0.04 / 0.6 | 0.03 / 0.2 | 0.1 / 0.5 | 0.007 / 0.2 | 0.01 / 0.2 | 0.005 / 0.3 | 0.1 / 0.7 | 0.1 / 1.0 |
wavelet.LHH_glszm_GrayLevelNonUniformity | wavelet.LHH_glszm_GrayLevelNonUniformity | Med [IQR] | 0.02 [0.01;0.04] | 0.2 [0.1;0.3] | 0.2 [0.2;0.3] | 0.1 [0.1;0.1] | 0.2 [0.2;0.4] | 0.1 [0.04;0.1] | 0.1 [0.03;0.1] | 0.1 [0.03;0.2] | 0.4 [0.3;0.5] | 0.4 [0.2;0.5] |
wavelet.LHH_glszm_GrayLevelNonUniformity | wavelet.LHH_glszm_GrayLevelNonUniformity | Mean (std) | 0.02 (0.02) | 0.2 (0.1) | 0.2 (0.1) | 0.1 (0.05) | 0.3 (0.2) | 0.1 (0.1) | 0.1 (0.04) | 0.1 (0.1) | 0.4 (0.1) | 0.4 (0.2) |
wavelet.LHH_glszm_GrayLevelNonUniformity | wavelet.LHH_glszm_GrayLevelNonUniformity | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
wavelet.LHH_firstorder_Skewness | wavelet.LHH_firstorder_Skewness | Min / Max | 0.3 / 0.7 | 0.4 / 0.9 | 0.3 / 0.6 | 0.3 / 0.6 | 0.4 / 0.8 | 0.4 / 0.8 | 0 / 1.0 | 0.2 / 0.5 | 0.4 / 0.6 | 0.4 / 0.6 |
wavelet.LHH_firstorder_Skewness | wavelet.LHH_firstorder_Skewness | Med [IQR] | 0.5 [0.4;0.5] | 0.5 [0.4;0.5] | 0.5 [0.4;0.5] | 0.5 [0.4;0.5] | 0.5 [0.5;0.7] | 0.5 [0.4;0.5] | 0.5 [0.4;0.5] | 0.4 [0.4;0.5] | 0.5 [0.4;0.5] | 0.4 [0.4;0.5] |
wavelet.LHH_firstorder_Skewness | wavelet.LHH_firstorder_Skewness | Mean (std) | 0.5 (0.1) | 0.5 (0.1) | 0.5 (0.1) | 0.5 (0.1) | 0.6 (0.1) | 0.5 (0.1) | 0.5 (0.2) | 0.4 (0.1) | 0.5 (0.05) | 0.5 (0.05) |
wavelet.LHH_firstorder_Skewness | wavelet.LHH_firstorder_Skewness | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
wavelet.HHH_glszm_GrayLevelNonUniformityNormalized | wavelet.HHH_glszm_GrayLevelNonUniformityNormalized | Min / Max | 0.5 / 0.9 | 0.2 / 1.0 | 0.1 / 0.9 | 0.04 / 0.8 | 0 / 0.4 | 0.5 / 0.9 | 0.04 / 0.6 | 0.5 / 0.9 | 0.05 / 0.7 | 0.1 / 0.5 |
wavelet.HHH_glszm_GrayLevelNonUniformityNormalized | wavelet.HHH_glszm_GrayLevelNonUniformityNormalized | Med [IQR] | 0.6 [0.5;0.6] | 0.6 [0.5;0.6] | 0.2 [0.1;0.4] | 0.2 [0.1;0.5] | 0.2 [0.1;0.3] | 0.6 [0.5;0.6] | 0.3 [0.2;0.4] | 0.6 [0.5;0.6] | 0.2 [0.1;0.3] | 0.3 [0.3;0.4] |
wavelet.HHH_glszm_GrayLevelNonUniformityNormalized | wavelet.HHH_glszm_GrayLevelNonUniformityNormalized | Mean (std) | 0.6 (0.1) | 0.6 (0.1) | 0.3 (0.2) | 0.3 (0.2) | 0.2 (0.1) | 0.6 (0.1) | 0.3 (0.2) | 0.6 (0.1) | 0.2 (0.1) | 0.3 (0.1) |
wavelet.HHH_glszm_GrayLevelNonUniformityNormalized | wavelet.HHH_glszm_GrayLevelNonUniformityNormalized | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
wavelet.LHH_glcm_Imc1 | wavelet.LHH_glcm_Imc1 | Min / Max | 0 / 0.9 | 0.5 / 0.9 | 0.5 / 0.9 | 0.3 / 0.9 | 0.5 / 0.8 | 0.3 / 0.9 | 0.1 / 0.8 | 0.5 / 1.0 | 0.4 / 0.9 | 0.4 / 1.0 |
wavelet.LHH_glcm_Imc1 | wavelet.LHH_glcm_Imc1 | Med [IQR] | 0.6 [0.5;0.6] | 0.8 [0.7;0.8] | 0.8 [0.7;0.8] | 0.7 [0.6;0.7] | 0.7 [0.6;0.7] | 0.7 [0.6;0.7] | 0.5 [0.3;0.7] | 0.8 [0.7;0.9] | 0.7 [0.7;0.8] | 0.7 [0.7;0.7] |
wavelet.LHH_glcm_Imc1 | wavelet.LHH_glcm_Imc1 | Mean (std) | 0.5 (0.2) | 0.8 (0.1) | 0.8 (0.1) | 0.7 (0.1) | 0.7 (0.1) | 0.6 (0.1) | 0.5 (0.2) | 0.8 (0.1) | 0.7 (0.1) | 0.7 (0.1) |
wavelet.LHH_glcm_Imc1 | wavelet.LHH_glcm_Imc1 | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
log.sigma.5.0.mm.3D_firstorder_Kurtosis | log.sigma.5.0.mm.3D_firstorder_Kurtosis | Min / Max | 0.004 / 0.1 | 0.03 / 1.0 | 0.04 / 0.4 | 0.02 / 0.1 | 0.05 / 0.4 | 0.007 / 0.2 | 0 / 0.2 | 0.02 / 0.4 | 0.03 / 0.6 | 0.03 / 0.8 |
log.sigma.5.0.mm.3D_firstorder_Kurtosis | log.sigma.5.0.mm.3D_firstorder_Kurtosis | Med [IQR] | 0.04 [0.03;0.04] | 0.1 [0.1;0.2] | 0.1 [0.1;0.1] | 0.04 [0.03;0.1] | 0.2 [0.1;0.2] | 0.1 [0.03;0.1] | 0.03 [0.02;0.1] | 0.1 [0.1;0.2] | 0.1 [0.1;0.2] | 0.1 [0.1;0.2] |
log.sigma.5.0.mm.3D_firstorder_Kurtosis | log.sigma.5.0.mm.3D_firstorder_Kurtosis | Mean (std) | 0.04 (0.01) | 0.1 (0.1) | 0.1 (0.1) | 0.05 (0.03) | 0.2 (0.1) | 0.1 (0.04) | 0.04 (0.04) | 0.1 (0.1) | 0.2 (0.1) | 0.2 (0.1) |
log.sigma.5.0.mm.3D_firstorder_Kurtosis | log.sigma.5.0.mm.3D_firstorder_Kurtosis | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
log.sigma.5.0.mm.3D_firstorder_90Percentile | log.sigma.5.0.mm.3D_firstorder_90Percentile | Min / Max | 0.4 / 1.0 | 0.8 / 0.9 | 0.8 / 0.9 | 0.7 / 0.9 | 0.9 / 0.9 | 0.6 / 0.9 | 0 / 1.0 | 0.8 / 0.9 | 0.8 / 1.0 | 0.8 / 1.0 |
log.sigma.5.0.mm.3D_firstorder_90Percentile | log.sigma.5.0.mm.3D_firstorder_90Percentile | Med [IQR] | 0.8 [0.8;0.9] | 0.9 [0.8;0.9] | 0.9 [0.8;0.9] | 0.8 [0.8;0.9] | 0.9 [0.9;0.9] | 0.8 [0.8;0.9] | 0.8 [0.8;0.9] | 0.9 [0.8;0.9] | 0.9 [0.9;0.9] | 0.9 [0.9;0.9] |
log.sigma.5.0.mm.3D_firstorder_90Percentile | log.sigma.5.0.mm.3D_firstorder_90Percentile | Mean (std) | 0.8 (0.1) | 0.9 (0.01) | 0.9 (0.03) | 0.8 (0.1) | 0.9 (0.02) | 0.8 (0.1) | 0.8 (0.2) | 0.9 (0.02) | 0.9 (0.03) | 0.9 (0.03) |
log.sigma.5.0.mm.3D_firstorder_90Percentile | log.sigma.5.0.mm.3D_firstorder_90Percentile | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis | wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis | Min / Max | 0.1 / 1.0 | 0.04 / 0.2 | 0.1 / 0.3 | 0.1 / 0.8 | 0 / 0.1 | 0.1 / 0.9 | 0.1 / 0.5 | 0.1 / 0.9 | 0.05 / 0.1 | 0.01 / 0.3 |
wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis | wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis | Med [IQR] | 0.3 [0.2;0.5] | 0.2 [0.1;0.2] | 0.2 [0.1;0.2] | 0.2 [0.1;0.3] | 0.03 [0.02;0.04] | 0.2 [0.2;0.3] | 0.2 [0.1;0.3] | 0.2 [0.2;0.5] | 0.1 [0.1;0.1] | 0.1 [0.1;0.1] |
wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis | wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 0.4 (0.3) | 0.2 (0.1) | 0.2 (0.1) | 0.2 (0.2) | 0.03 (0.02) | 0.3 (0.2) | 0.2 (0.1) | 0.3 (0.2) | 0.1 (0.02) | 0.1 (0.1) |
wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis | wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
wavelet.HLH_glcm_DifferenceAverage | wavelet.HLH_glcm_DifferenceAverage | Min / Max | 0 / 0.7 | 0.1 / 0.3 | 0.2 / 0.4 | 0.1 / 0.5 | 0.2 / 0.4 | 0.02 / 0.2 | 0.1 / 1.0 | 0.1 / 0.3 | 0.2 / 0.4 | 0.3 / 0.7 |
wavelet.HLH_glcm_DifferenceAverage | wavelet.HLH_glcm_DifferenceAverage | Med [IQR] | 0.2 [0.1;0.3] | 0.2 [0.2;0.2] | 0.3 [0.2;0.3] | 0.3 [0.2;0.3] | 0.3 [0.2;0.3] | 0.1 [0.1;0.2] | 0.5 [0.3;0.6] | 0.2 [0.2;0.2] | 0.3 [0.3;0.3] | 0.4 [0.3;0.4] |
wavelet.HLH_glcm_DifferenceAverage | wavelet.HLH_glcm_DifferenceAverage | Mean (std) | 0.2 (0.2) | 0.2 (0.04) | 0.3 (0.04) | 0.3 (0.1) | 0.3 (0.1) | 0.1 (0.04) | 0.5 (0.2) | 0.2 (0.05) | 0.3 (0.04) | 0.4 (0.1) |
wavelet.HLH_glcm_DifferenceAverage | wavelet.HLH_glcm_DifferenceAverage | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
wavelet.HHH_firstorder_RootMeanSquared | wavelet.HHH_firstorder_RootMeanSquared | Min / Max | 0 / 1.0 | 0.7 / 0.8 | 0.7 / 0.9 | 0.6 / 1.0 | 0.7 / 0.8 | 0.6 / 0.9 | 0.2 / 1.0 | 0.8 / 0.9 | 0.8 / 0.9 | 0.7 / 0.9 |
wavelet.HHH_firstorder_RootMeanSquared | wavelet.HHH_firstorder_RootMeanSquared | Med [IQR] | 0.7 [0.6;0.8] | 0.8 [0.8;0.8] | 0.8 [0.8;0.8] | 0.8 [0.7;0.8] | 0.8 [0.8;0.8] | 0.8 [0.8;0.8] | 0.8 [0.6;0.8] | 0.8 [0.8;0.8] | 0.8 [0.8;0.8] | 0.8 [0.8;0.9] |
wavelet.HHH_firstorder_RootMeanSquared | wavelet.HHH_firstorder_RootMeanSquared | Mean (std) | 0.6 (0.2) | 0.8 (0.02) | 0.8 (0.03) | 0.8 (0.1) | 0.8 (0.03) | 0.8 (0.05) | 0.7 (0.2) | 0.8 (0.02) | 0.8 (0.03) | 0.8 (0.04) |
wavelet.HHH_firstorder_RootMeanSquared | wavelet.HHH_firstorder_RootMeanSquared | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
wavelet.LHL_gldm_DependenceVariance | wavelet.LHL_gldm_DependenceVariance | Min / Max | 0.008 / 0.6 | 0.2 / 0.9 | 0.1 / 0.8 | 0.03 / 0.8 | 0.1 / 0.7 | 0.1 / 1.0 | 0 / 0.5 | 0.1 / 0.7 | 0.2 / 0.8 | 0.1 / 0.7 |
wavelet.LHL_gldm_DependenceVariance | wavelet.LHL_gldm_DependenceVariance | Med [IQR] | 0.1 [0.1;0.2] | 0.6 [0.4;0.7] | 0.4 [0.3;0.5] | 0.2 [0.2;0.3] | 0.3 [0.2;0.6] | 0.4 [0.3;0.6] | 0.1 [0.04;0.2] | 0.3 [0.2;0.4] | 0.4 [0.3;0.6] | 0.2 [0.2;0.4] |
wavelet.LHL_gldm_DependenceVariance | wavelet.LHL_gldm_DependenceVariance | Mean (std) | 0.2 (0.1) | 0.5 (0.2) | 0.4 (0.2) | 0.3 (0.2) | 0.4 (0.2) | 0.4 (0.2) | 0.2 (0.2) | 0.3 (0.2) | 0.4 (0.2) | 0.3 (0.2) |
wavelet.LHL_gldm_DependenceVariance | wavelet.LHL_gldm_DependenceVariance | N (NA) | 36 (0) | 50 (0) | 53 (0) | 37 (0) | 13 (0) | 64 (0) | 20 (0) | 30 (0) | 33 (0) | 41 (0) |
Histology | Histology | adenocarcinoma | 8 (15.69%) | 6 (11.76%) | 4 (7.84%) | 5 (9.80%) | 3 (5.88%) | 10 (19.61%) | 1 (1.96%) | 5 (9.80%) | 3 (5.88%) | 6 (11.76%) |
Histology | Histology | large cell | 6 (5.26%) | 13 (11.40%) | 16 (14.04%) | 14 (12.28%) | 1 (0.88%) | 18 (15.79%) | 7 (6.14%) | 11 (9.65%) | 9 (7.89%) | 19 (16.67%) |
Histology | Histology | nos | 7 (11.48%) | 7 (11.48%) | 10 (16.39%) | 9 (14.75%) | 1 (1.64%) | 4 (6.56%) | 5 (8.20%) | 7 (11.48%) | 8 (13.11%) | 3 (4.92%) |
Histology | Histology | squamous cell carcinoma | 15 (9.93%) | 24 (15.89%) | 23 (15.23%) | 9 (5.96%) | 8 (5.30%) | 32 (21.19%) | 7 (4.64%) | 7 (4.64%) | 13 (8.61%) | 13 (8.61%) |
Manufacturer | Manufacturer | CMS Inc. | 3 (3.57%) | 5 (5.95%) | 18 (21.43%) | 9 (10.71%) | 6 (7.14%) | 4 (4.76%) | 6 (7.14%) | 4 (4.76%) | 14 (16.67%) | 15 (17.86%) |
Manufacturer | Manufacturer | SIEMENS | 33 (11.26%) | 45 (15.36%) | 35 (11.95%) | 28 (9.56%) | 7 (2.39%) | 60 (20.48%) | 14 (4.78%) | 26 (8.87%) | 19 (6.48%) | 26 (8.87%) |
Overall.Stage | Overall.Stage | I | 7 (10.77%) | 2 (3.08%) | 6 (9.23%) | 14 (21.54%) | 4 (6.15%) | 11 (16.92%) | 4 (6.15%) | 6 (9.23%) | 8 (12.31%) | 3 (4.62%) |
Overall.Stage | Overall.Stage | II | 4 (10.53%) | 7 (18.42%) | 9 (23.68%) | 3 (7.89%) | 4 (10.53%) | 4 (10.53%) | 1 (2.63%) | 0 (0%) | 5 (13.16%) | 1 (2.63%) |
Overall.Stage | Overall.Stage | IIIa | 13 (12.04%) | 10 (9.26%) | 16 (14.81%) | 10 (9.26%) | 2 (1.85%) | 26 (24.07%) | 6 (5.56%) | 8 (7.41%) | 4 (3.70%) | 13 (12.04%) |
Overall.Stage | Overall.Stage | IIIb | 12 (7.23%) | 31 (18.67%) | 22 (13.25%) | 10 (6.02%) | 3 (1.81%) | 23 (13.86%) | 9 (5.42%) | 16 (9.64%) | 16 (9.64%) | 24 (14.46%) |
clinical.T.Stage | clinical.T.Stage | 1 | 25 (36.76%) | 0 (0%) | 4 (5.88%) | 15 (22.06%) | 1 (1.47%) | 11 (16.18%) | 9 (13.24%) | 2 (2.94%) | 0 (0%) | 1 (1.47%) |
clinical.T.Stage | clinical.T.Stage | 2 | 6 (4.03%) | 14 (9.40%) | 23 (15.44%) | 16 (10.74%) | 5 (3.36%) | 35 (23.49%) | 2 (1.34%) | 15 (10.07%) | 17 (11.41%) | 16 (10.74%) |
clinical.T.Stage | clinical.T.Stage | 3 | 1 (2.00%) | 13 (26.00%) | 7 (14.00%) | 3 (6.00%) | 4 (8.00%) | 6 (12.00%) | 4 (8.00%) | 4 (8.00%) | 5 (10.00%) | 3 (6.00%) |
clinical.T.Stage | clinical.T.Stage | 4 | 4 (3.64%) | 23 (20.91%) | 19 (17.27%) | 3 (2.73%) | 3 (2.73%) | 12 (10.91%) | 5 (4.55%) | 9 (8.18%) | 11 (10.00%) | 21 (19.09%) |
Clinical.N.Stage | Clinical.N.Stage | 0 | 11 (7.91%) | 16 (11.51%) | 26 (18.71%) | 17 (12.23%) | 7 (5.04%) | 15 (10.79%) | 7 (5.04%) | 13 (9.35%) | 19 (13.67%) | 8 (5.76%) |
Clinical.N.Stage | Clinical.N.Stage | 1_2 | 12 (7.69%) | 22 (14.10%) | 22 (14.10%) | 13 (8.33%) | 6 (3.85%) | 32 (20.51%) | 7 (4.49%) | 10 (6.41%) | 9 (5.77%) | 23 (14.74%) |
Clinical.N.Stage | Clinical.N.Stage | 3 | 13 (15.85%) | 12 (14.63%) | 5 (6.10%) | 7 (8.54%) | 0 (0%) | 17 (20.73%) | 6 (7.32%) | 7 (8.54%) | 5 (6.10%) | 10 (12.20%) |
Then I want to evaluate possible relationship between clusters generated and histology class.
# I generate a cross table to check distribution of histology classes with partitions
table_hist_clust_res_varsel <- table(lung_data_res_varsel$Histology,lung_data_res_varsel$partition)
addmargins(prop.table(table_hist_clust_res_varsel)) %>% round(3) %>% kable(full_with = FALSE) %>% kable_classic_2()
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | Sum | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| adenocarcinoma | 0.021 | 0.016 | 0.011 | 0.013 | 0.008 | 0.027 | 0.003 | 0.013 | 0.008 | 0.016 | 0.135 |
| large cell | 0.016 | 0.034 | 0.042 | 0.037 | 0.003 | 0.048 | 0.019 | 0.029 | 0.024 | 0.050 | 0.302 |
| nos | 0.019 | 0.019 | 0.027 | 0.024 | 0.003 | 0.011 | 0.013 | 0.019 | 0.021 | 0.008 | 0.162 |
| squamous cell carcinoma | 0.040 | 0.064 | 0.061 | 0.024 | 0.021 | 0.085 | 0.019 | 0.019 | 0.034 | 0.034 | 0.401 |
| Sum | 0.095 | 0.133 | 0.141 | 0.098 | 0.034 | 0.170 | 0.053 | 0.080 | 0.088 | 0.109 | 1.000 |
# I display distribution of histology class within different clusters
ggplot(lung_data_res_varsel,aes(partition, fill = Histology)) +
geom_bar(position = 'fill',alpha = .5) +
coord_flip()+
theme_bw()
ggsave("figures/Fig25.tiff",width = 100, height = 50, device="tiff", dpi=300, units = "mm", scale = 1)
# I continue with a correspondence analysis and generate an assymetric representation using
# columns (clusters) repsented with the principal coordinates and histology classes
# represented with standard coordinates
ca.res_varsel <- ca(table_hist_clust_res_varsel)
tiff(filename = "figures/Fig26.tiff", width = 200, height = 200, units = "mm", res = 300)
plot(ca.res_varsel, map= "colprincipal")
fig <- dev.off()
plot(ca.res_varsel, map= "colprincipal")
# From CA analisis I get number of clusters with the greatest inertias to further compare cluster characteristics
clust_top_inertias <- data.frame(clust = ca.res_varsel$colnames, inertia = get_ca_col(ca.res_varsel)$inertia) %>% arrange(desc(inertia)) %>% top_n(2)
# After evaluating main clusters associated with different histology classes we can focus our analysis in
# describing differences between these clusters:
lung_data_res_varsel_selected <- lung_data_res_varsel %>% filter(partition %in% clust_top_inertias$clust) %>% droplevels()
lung_data_res_varsel_selected_table <- table(lung_data_res_varsel_selected$Histology,lung_data_res_varsel_selected$partition)
# I evaluate agreement using adjusted rand index (ARI) coefficient
cat('ARI (Histology class, Partitions):',ARI(lung_data_res_varsel_selected$Histology,lung_data_res_varsel_selected$partition))
## ARI (Histology class, Partitions): 0.02430256
# And I generate a cross table focusing on these clusters
(crosstab1<- crosstable(lung_data_res_varsel_selected %>% select(.,-c(deadstatus.event,Survival.time)),
by = partition, test = TRUE))
## # A tibble: 147 × 6
## .id label variable `6` `10` test
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 gender gender female 22 (… 13 (… "p v…
## 2 gender gender male 42 (… 28 (… "p v…
## 3 age age Min / M… 0.2 … 0.2 … "p v…
## 4 age age Med [IQ… 0.6 … 0.6 … "p v…
## 5 age age Mean (s… 0.6 … 0.6 … "p v…
## 6 age age N (NA) 64 (… 41 (… "p v…
## 7 original_shape_VoxelVolume original_shape_VoxelVo… Min / M… 0.3 … 0.5 … "p v…
## 8 original_shape_VoxelVolume original_shape_VoxelVo… Med [IQ… 0.5 … 0.7 … "p v…
## 9 original_shape_VoxelVolume original_shape_VoxelVo… Mean (s… 0.5 … 0.7 … "p v…
## 10 original_shape_VoxelVolume original_shape_VoxelVo… N (NA) 64 (… 41 (… "p v…
## # … with 137 more rows
(table23 <- crosstab1 %>% separate(test,
into = c('pvalue',
'test'),
sep = "\n") %>%
separate(pvalue,
into = c('string',
'pval'),
sep = ":",
convert = TRUE) %>%
arrange(pval) %>%
filter(variable != 'Med [IQR]',
variable != 'N (NA)',
variable != 'Min / Max') %>%
dplyr::select(.,-c(string,.id)) %>% kable(full_with = FALSE) %>% kable_classic_2())
| label | variable | 6 | 10 | pval | test |
|---|---|---|---|---|---|
| original_shape_VoxelVolume | Mean (std) | 0.5 (0.1) | 0.7 (0.1) | <0.0001 | (Two Sample t-test) |
| wavelet.HHL_glcm_ClusterProminence | Mean (std) | 0.03 (0.02) | 0.2 (0.1) | <0.0001 | (Wilcoxon rank sum test) |
| log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis | Mean (std) | 0.1 (0.1) | 0.3 (0.2) | <0.0001 | (Wilcoxon rank sum test) |
| log.sigma.2.0.mm.3D_glcm_Autocorrelation | Mean (std) | 0.2 (0.1) | 0.3 (0.1) | <0.0001 | (Two Sample t-test) |
| wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis | Mean (std) | 0.5 (0.1) | 0.6 (0.1) | <0.0001 | (Two Sample t-test) |
| wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis | Mean (std) | 0.01 (0.006) | 0.1 (0.04) | <0.0001 | (Wilcoxon rank sum test) |
| wavelet.HHH_glszm_GrayLevelNonUniformity | Mean (std) | 0.02 (0.01) | 0.2 (0.2) | <0.0001 | (Wilcoxon rank sum test) |
| wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 0.2 (0.1) | 0.4 (0.1) | <0.0001 | (Two Sample t-test) |
| wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis | Mean (std) | 0.1 (0.04) | 0.02 (0.01) | <0.0001 | (Wilcoxon rank sum test) |
| wavelet.HHH_glcm_ClusterProminence | Mean (std) | 0.1 (0.03) | 0.1 (0.04) | <0.0001 | (Wilcoxon rank sum test) |
| wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 0.2 (0.1) | 0.3 (0.1) | <0.0001 | (Two Sample t-test) |
| wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis | Mean (std) | 0.5 (0.1) | 0.6 (0.1) | <0.0001 | (Two Sample t-test) |
| wavelet.LLL_glcm_ClusterProminence | Mean (std) | 0.6 (0.1) | 0.7 (0.1) | <0.0001 | (Wilcoxon rank sum test) |
| wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 0.2 (0.1) | 0.03 (0.01) | <0.0001 | (Wilcoxon rank sum test) |
| wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis | Mean (std) | 0.2 (0.1) | 0.1 (0.03) | <0.0001 | (Wilcoxon rank sum test) |
| wavelet.HHH_glrlm_GrayLevelVariance | Mean (std) | 0.02 (1e-04) | 0.2 (0.1) | <0.0001 | (Wilcoxon rank sum test) |
| wavelet.HHH_glcm_Imc1 | Mean (std) | 0.8 (0.1) | 0.7 (0.1) | <0.0001 | (Wilcoxon rank sum test) |
| log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 0.2 (0.1) | 0.4 (0.1) | <0.0001 | (Two Sample t-test) |
| wavelet.LHH_glszm_GrayLevelNonUniformity | Mean (std) | 0.1 (0.1) | 0.4 (0.2) | <0.0001 | (Wilcoxon rank sum test) |
| wavelet.HHH_glszm_GrayLevelNonUniformityNormalized | Mean (std) | 0.6 (0.1) | 0.3 (0.1) | <0.0001 | (Wilcoxon rank sum test) |
| log.sigma.5.0.mm.3D_firstorder_Kurtosis | Mean (std) | 0.1 (0.04) | 0.2 (0.1) | <0.0001 | (Wilcoxon rank sum test) |
| log.sigma.5.0.mm.3D_firstorder_90Percentile | Mean (std) | 0.8 (0.1) | 0.9 (0.03) | <0.0001 | (Wilcoxon rank sum test) |
| wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 0.3 (0.2) | 0.1 (0.1) | <0.0001 | (Wilcoxon rank sum test) |
| wavelet.HLH_glcm_DifferenceAverage | Mean (std) | 0.1 (0.04) | 0.4 (0.1) | <0.0001 | (Wilcoxon rank sum test) |
| wavelet.HHH_firstorder_RootMeanSquared | Mean (std) | 0.8 (0.05) | 0.8 (0.04) | <0.0001 | (Wilcoxon rank sum test) |
| Manufacturer | CMS Inc. | 4 (21.05%) | 15 (78.95%) | 0.0001 | (Pearson’s Chi-squared test) |
| Manufacturer | SIEMENS | 60 (69.77%) | 26 (30.23%) | 0.0001 | (Pearson’s Chi-squared test) |
| wavelet.LHL_gldm_DependenceVariance | Mean (std) | 0.4 (0.2) | 0.3 (0.2) | 0.0002 | (Wilcoxon rank sum test) |
| clinical.T.Stage | 1 | 11 (91.67%) | 1 (8.33%) | 0.0017 | (Fisher’s Exact Test for Count Data) |
| clinical.T.Stage | 2 | 35 (68.63%) | 16 (31.37%) | 0.0017 | (Fisher’s Exact Test for Count Data) |
| clinical.T.Stage | 3 | 6 (66.67%) | 3 (33.33%) | 0.0017 | (Fisher’s Exact Test for Count Data) |
| clinical.T.Stage | 4 | 12 (36.36%) | 21 (63.64%) | 0.0017 | (Fisher’s Exact Test for Count Data) |
| wavelet.LHH_glcm_Imc1 | Mean (std) | 0.6 (0.1) | 0.7 (0.1) | 0.0035 | (Wilcoxon rank sum test) |
| wavelet.LHH_firstorder_Skewness | Mean (std) | 0.5 (0.1) | 0.5 (0.05) | 0.0077 | (Wilcoxon rank sum test) |
| log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 0.03 (0.02) | 0.03 (0.02) | 0.0292 | (Wilcoxon rank sum test) |
| Overall.Stage | I | 11 (78.57%) | 3 (21.43%) | 0.1191 | (Fisher’s Exact Test for Count Data) |
| Overall.Stage | II | 4 (80.00%) | 1 (20.00%) | 0.1191 | (Fisher’s Exact Test for Count Data) |
| Overall.Stage | IIIa | 26 (66.67%) | 13 (33.33%) | 0.1191 | (Fisher’s Exact Test for Count Data) |
| Overall.Stage | IIIb | 23 (48.94%) | 24 (51.06%) | 0.1191 | (Fisher’s Exact Test for Count Data) |
| Histology | adenocarcinoma | 10 (62.50%) | 6 (37.50%) | 0.2150 | (Fisher’s Exact Test for Count Data) |
| Histology | large cell | 18 (48.65%) | 19 (51.35%) | 0.2150 | (Fisher’s Exact Test for Count Data) |
| Histology | nos | 4 (57.14%) | 3 (42.86%) | 0.2150 | (Fisher’s Exact Test for Count Data) |
| Histology | squamous cell carcinoma | 32 (71.11%) | 13 (28.89%) | 0.2150 | (Fisher’s Exact Test for Count Data) |
| age | Mean (std) | 0.6 (0.2) | 0.6 (0.1) | 0.2274 | (Two Sample t-test) |
| log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized | Mean (std) | 0.4 (0.1) | 0.4 (0.2) | 0.5356 | (Two Sample t-test) |
| original_glcm_Imc1 | Mean (std) | 0.6 (0.1) | 0.6 (0.1) | 0.5677 | (Wilcoxon rank sum test) |
| gender | female | 22 (62.86%) | 13 (37.14%) | 0.7773 | (Pearson’s Chi-squared test) |
| gender | male | 42 (60.00%) | 28 (40.00%) | 0.7773 | (Pearson’s Chi-squared test) |
| Clinical.N.Stage | 0 | 15 (65.22%) | 8 (34.78%) | 0.8191 | (Pearson’s Chi-squared test) |
| Clinical.N.Stage | 1_2 | 32 (58.18%) | 23 (41.82%) | 0.8191 | (Pearson’s Chi-squared test) |
| Clinical.N.Stage | 3 | 17 (62.96%) | 10 (37.04%) | 0.8191 | (Pearson’s Chi-squared test) |
table23 %>% save_kable("tables/table23.png")
I evaluate relationship with Overall stage variable:
# I generate a cross table to check distribution of histology classes with partitions
table_stage_clust_res_varsel <- table(lung_data_res_varsel$Overall.Stage,lung_data_res_varsel$partition)
addmargins(prop.table(table_stage_clust_res_varsel)) %>% round(3) %>% kable(full_with = FALSE) %>% kable_classic_2()
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | Sum | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| I | 0.019 | 0.005 | 0.016 | 0.037 | 0.011 | 0.029 | 0.011 | 0.016 | 0.021 | 0.008 | 0.172 |
| II | 0.011 | 0.019 | 0.024 | 0.008 | 0.011 | 0.011 | 0.003 | 0.000 | 0.013 | 0.003 | 0.101 |
| IIIa | 0.034 | 0.027 | 0.042 | 0.027 | 0.005 | 0.069 | 0.016 | 0.021 | 0.011 | 0.034 | 0.286 |
| IIIb | 0.032 | 0.082 | 0.058 | 0.027 | 0.008 | 0.061 | 0.024 | 0.042 | 0.042 | 0.064 | 0.440 |
| Sum | 0.095 | 0.133 | 0.141 | 0.098 | 0.034 | 0.170 | 0.053 | 0.080 | 0.088 | 0.109 | 1.000 |
# I display distribution of histology class within different clusters
ggplot(lung_data_res_varsel,aes(partition, fill = Overall.Stage)) +
geom_bar(position = 'fill',alpha = .5) +
coord_flip()+
theme_bw()
ggsave("figures/Fig31.1.tiff",width = 100, height = 50, device="tiff", dpi=300, units = "mm", scale = 1)
# I continue with a correspondence analysis and generate an assymetric representation using
# columns (clusters) repsented with the principal coordinates and histology classes
# represented with standard coordinates
ca.res_varsel_stage <- ca(table_stage_clust_res_varsel)
tiff(filename = "figures/Fig32.1.tiff", width = 200, height = 200, units = "mm", res = 300)
plot(ca.res_varsel_stage, map= "colprincipal")
fig <- dev.off()
plot(ca.res_varsel_stage, map= "colprincipal")
# From CA analisis I get number of clusters with the greatest inertias to further compare cluster characteristics
clust_top_inertias_stage <- data.frame(clust = ca.res_varsel_stage$colnames, inertia = get_ca_col(ca.res_varsel_stage)$inertia) %>% arrange(desc(inertia)) %>% top_n(2)
# After evaluating main clusters associated with different histology classes we can focus our analysis in
# describing differences between these clusters:
lung_data_res_varsel_selected_stage <- lung_data_res_varsel %>% filter(partition %in% clust_top_inertias_stage$clust) %>% droplevels()
lung_data_res_varsel_selected_table_stage <- table(lung_data_res_varsel_selected$Overall.Stage,lung_data_res_varsel_selected$partition)
# I evaluate agreement using adjusted rand index (ARI) coefficient
cat('ARI (Histology class, Partitions):',ARI(lung_data_res_varsel_selected_stage$Overall.Stage,lung_data_res_varsel_selected_stage$partition))
## ARI (Histology class, Partitions): 0.1299822
# And I generate a cross table focusing on these clusters
crosstab1.1<- crosstable(lung_data_res_varsel_selected_stage %>% select(.,-c(deadstatus.event,Survival.time)),
by = partition, test = TRUE)
(table23.1 <- crosstab1.1 %>% separate(test,
into = c('pvalue',
'test'),
sep = "\n") %>%
separate(pvalue,
into = c('string',
'pval'),
sep = ":",
convert = TRUE) %>%
arrange(pval) %>%
filter(variable != 'Med [IQR]',
variable != 'N (NA)',
variable != 'Min / Max') %>%
dplyr::select(.,-c(string,.id)) %>% kable(full_with = FALSE) %>% kable_classic_2())
| label | variable | 2 | 4 | pval | test |
|---|---|---|---|---|---|
| original_shape_VoxelVolume | Mean (std) | 0.8 (0.1) | 0.4 (0.1) | <0.0001 | (Two Sample t-test) |
| wavelet.HHL_glcm_ClusterProminence | Mean (std) | 0.02 (0.01) | 0.1 (0.1) | <0.0001 | (Wilcoxon rank sum test) |
| log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis | Mean (std) | 0.5 (0.2) | 0.1 (0.1) | <0.0001 | (Wilcoxon rank sum test) |
| wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis | Mean (std) | 0.8 (0.1) | 0.4 (0.1) | <0.0001 | (Two Sample t-test) |
| wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis | Mean (std) | 0.01 (0.006) | 0.1 (0.03) | <0.0001 | (Welch Two Sample t-test) |
| wavelet.HHH_glszm_GrayLevelNonUniformity | Mean (std) | 0.04 (0.03) | 0.01 (0.01) | <0.0001 | (Wilcoxon rank sum test) |
| wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 0.4 (0.1) | 0.2 (0.1) | <0.0001 | (Two Sample t-test) |
| wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis | Mean (std) | 0.02 (0.01) | 0.1 (0.04) | <0.0001 | (Wilcoxon rank sum test) |
| wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 0.4 (0.1) | 0.2 (0.1) | <0.0001 | (Welch Two Sample t-test) |
| wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis | Mean (std) | 0.8 (0.1) | 0.5 (0.1) | <0.0001 | (Two Sample t-test) |
| wavelet.LLL_glcm_ClusterProminence | Mean (std) | 0.5 (0.2) | 0.6 (0.1) | <0.0001 | (Welch Two Sample t-test) |
| wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis | Mean (std) | 0.1 (0.02) | 0.2 (0.1) | <0.0001 | (Wilcoxon rank sum test) |
| log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 0.5 (0.1) | 0.2 (0.1) | <0.0001 | (Welch Two Sample t-test) |
| wavelet.LHH_glszm_GrayLevelNonUniformity | Mean (std) | 0.2 (0.1) | 0.1 (0.05) | <0.0001 | (Wilcoxon rank sum test) |
| wavelet.HHH_glszm_GrayLevelNonUniformityNormalized | Mean (std) | 0.6 (0.1) | 0.3 (0.2) | <0.0001 | (Wilcoxon rank sum test) |
| wavelet.LHH_glcm_Imc1 | Mean (std) | 0.8 (0.1) | 0.7 (0.1) | <0.0001 | (Wilcoxon rank sum test) |
| log.sigma.5.0.mm.3D_firstorder_Kurtosis | Mean (std) | 0.1 (0.1) | 0.05 (0.03) | <0.0001 | (Wilcoxon rank sum test) |
| wavelet.HLH_glcm_DifferenceAverage | Mean (std) | 0.2 (0.04) | 0.3 (0.1) | <0.0001 | (Wilcoxon rank sum test) |
| wavelet.LHL_gldm_DependenceVariance | Mean (std) | 0.5 (0.2) | 0.3 (0.2) | <0.0001 | (Wilcoxon rank sum test) |
| clinical.T.Stage | 1 | 0 (0%) | 15 (100.00%) | <0.0001 | (Pearson’s Chi-squared test) |
| clinical.T.Stage | 2 | 14 (46.67%) | 16 (53.33%) | <0.0001 | (Pearson’s Chi-squared test) |
| clinical.T.Stage | 3 | 13 (81.25%) | 3 (18.75%) | <0.0001 | (Pearson’s Chi-squared test) |
| clinical.T.Stage | 4 | 23 (88.46%) | 3 (11.54%) | <0.0001 | (Pearson’s Chi-squared test) |
| Overall.Stage | I | 2 (12.50%) | 14 (87.50%) | 0.0001 | (Fisher’s Exact Test for Count Data) |
| Overall.Stage | II | 7 (70.00%) | 3 (30.00%) | 0.0001 | (Fisher’s Exact Test for Count Data) |
| Overall.Stage | IIIa | 10 (50.00%) | 10 (50.00%) | 0.0001 | (Fisher’s Exact Test for Count Data) |
| Overall.Stage | IIIb | 31 (75.61%) | 10 (24.39%) | 0.0001 | (Fisher’s Exact Test for Count Data) |
| wavelet.HHH_glrlm_GrayLevelVariance | Mean (std) | 0.02 (4e-04) | 0.1 (0.05) | 0.0011 | (Wilcoxon rank sum test) |
| original_glcm_Imc1 | Mean (std) | 0.6 (0.1) | 0.7 (0.1) | 0.0016 | (Two Sample t-test) |
| log.sigma.5.0.mm.3D_firstorder_90Percentile | Mean (std) | 0.9 (0.01) | 0.8 (0.1) | 0.0071 | (Welch Two Sample t-test) |
| gender | female | 13 (39.39%) | 20 (60.61%) | 0.0077 | (Pearson’s Chi-squared test) |
| gender | male | 37 (68.52%) | 17 (31.48%) | 0.0077 | (Pearson’s Chi-squared test) |
| wavelet.LHH_firstorder_Skewness | Mean (std) | 0.5 (0.1) | 0.5 (0.1) | 0.0134 | (Wilcoxon rank sum test) |
| log.sigma.2.0.mm.3D_glcm_Autocorrelation | Mean (std) | 0.2 (0.1) | 0.2 (0.1) | 0.0226 | (Two Sample t-test) |
| Manufacturer | CMS Inc. | 5 (35.71%) | 9 (64.29%) | 0.0722 | (Pearson’s Chi-squared test) |
| Manufacturer | SIEMENS | 45 (61.64%) | 28 (38.36%) | 0.0722 | (Pearson’s Chi-squared test) |
| wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 0.1 (0.04) | 0.1 (0.03) | 0.1084 | (Wilcoxon rank sum test) |
| wavelet.HHH_glcm_Imc1 | Mean (std) | 0.8 (0.1) | 0.8 (0.2) | 0.1084 | (Wilcoxon rank sum test) |
| age | Mean (std) | 0.6 (0.2) | 0.6 (0.2) | 0.1103 | (Wilcoxon rank sum test) |
| Histology | adenocarcinoma | 6 (54.55%) | 5 (45.45%) | 0.1386 | (Fisher’s Exact Test for Count Data) |
| Histology | large cell | 13 (48.15%) | 14 (51.85%) | 0.1386 | (Fisher’s Exact Test for Count Data) |
| Histology | nos | 7 (43.75%) | 9 (56.25%) | 0.1386 | (Fisher’s Exact Test for Count Data) |
| Histology | squamous cell carcinoma | 24 (72.73%) | 9 (27.27%) | 0.1386 | (Fisher’s Exact Test for Count Data) |
| wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 0.2 (0.1) | 0.2 (0.2) | 0.1421 | (Wilcoxon rank sum test) |
| wavelet.HHH_firstorder_RootMeanSquared | Mean (std) | 0.8 (0.02) | 0.8 (0.1) | 0.2327 | (Wilcoxon rank sum test) |
| log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 0.03 (0.02) | 0.1 (0.1) | 0.2871 | (Wilcoxon rank sum test) |
| wavelet.HHH_glcm_ClusterProminence | Mean (std) | 0.1 (0.03) | 0.1 (0.03) | 0.3235 | (Wilcoxon rank sum test) |
| Clinical.N.Stage | 0 | 16 (48.48%) | 17 (51.52%) | 0.4154 | (Pearson’s Chi-squared test) |
| Clinical.N.Stage | 1_2 | 22 (62.86%) | 13 (37.14%) | 0.4154 | (Pearson’s Chi-squared test) |
| Clinical.N.Stage | 3 | 12 (63.16%) | 7 (36.84%) | 0.4154 | (Pearson’s Chi-squared test) |
| log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized | Mean (std) | 0.5 (0.1) | 0.5 (0.2) | 0.7863 | (Two Sample t-test) |
table23.1 %>% save_kable("tables/table23.1.png")
Finally I want to analyze survival probabilities among different clusters.
# I generate a specific dataframe including survival, dead status event and partition variables
df_surv_res_varsel <- data.frame(Survival.time = lung_data_no_out$Survival.time,
deadstatus.event = lung_data_no_out$deadstatus.event,
partition = res_varsel_select@partitions@zMAP)
# I create a survival object, and use it as response variable
# to create Kaplan-Meier survival curves for each cluster
sfit_res_varsel <- survfit(Surv(Survival.time, as.numeric(deadstatus.event)) ~ partition,
data = df_surv_res_varsel)
# I plot survival curve including confidence intervals for each curve and
# p value result from log-rank test to evaluate difference in survival between groups
tiff(filename = "figures/Fig27.tiff", width = 300, height = 200, units = "mm", res = 300)
(surv <- ggsurvplot(sfit_res_varsel,
surv.median.line = 'hv',
pval=TRUE,
risk.table='percentage',
surv.plot.height = 0.7,
tables.height = 0.3))
fig <- dev.off()
(surv <- ggsurvplot(sfit_res_varsel,
surv.median.line = 'hv',
pval=TRUE,
risk.table='percentage',
surv.plot.height = 0.7,
tables.height = 0.3))
# I select clusters with minimum and maximum median survival to compare
min_max_surv <- surv_median(sfit_res_varsel) %>%
filter(median == min(median) | median == max(median)) %>%
select(strata) %>%
separate(strata,
into = c('string',
'partition_num'),
sep = "=",
convert = TRUE)
min_max_surv$partition_num
## [1] 7 9
# I repeat log-rank test comparing only the two most extreme median survival groups
surv_pvalue(
survfit(Surv(Survival.time, as.numeric(deadstatus.event)) ~ partition,
data = df_surv_res_varsel %>% filter(partition %in% min_max_surv$partition_num)),
method = "survdiff",
test.for.trend = FALSE,
combine = FALSE
)
## variable pval method pval.txt
## 1 partition 0.0333741 Log-rank p = 0.033
# And describe composition and difference for the clusters with most extreme results on survival analisis
crosstab2<- crosstable(lung_data_res_varsel %>% filter(partition %in% min_max_surv$partition_num) %>% select(.,-c(deadstatus.event,Survival.time)) %>% droplevels(),
by = partition, test = TRUE)
(table24 <- crosstab2 %>% separate(test,
into = c('pvalue',
'test'),
sep = "\n") %>%
separate(pvalue,
into = c('string',
'pval'),
sep = ":",
convert = TRUE) %>%
arrange(pval) %>%
filter(variable != 'Med [IQR]',
variable != 'N (NA)',
variable != 'Min / Max') %>%
dplyr::select(.,-c(string,.id)) %>% kable(full_with = FALSE) %>% kable_classic_2())
| label | variable | 7 | 9 | pval | test |
|---|---|---|---|---|---|
| original_shape_VoxelVolume | Mean (std) | 0.3 (0.1) | 0.8 (0.1) | <0.0001 | (Welch Two Sample t-test) |
| wavelet.HHL_glcm_ClusterProminence | Mean (std) | 0.3 (0.2) | 0.1 (0.04) | <0.0001 | (Welch Two Sample t-test) |
| log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis | Mean (std) | 0.03 (0.1) | 0.4 (0.1) | <0.0001 | (Wilcoxon rank sum exact test) |
| wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis | Mean (std) | 0.2 (0.2) | 0.8 (0.1) | <0.0001 | (Wilcoxon rank sum exact test) |
| wavelet.HHH_glszm_GrayLevelNonUniformity | Mean (std) | 0.03 (0.04) | 0.1 (0.1) | <0.0001 | (Wilcoxon rank sum test) |
| wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 0.3 (0.1) | 0.5 (0.1) | <0.0001 | (Wilcoxon rank sum exact test) |
| wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis | Mean (std) | 0.1 (0.1) | 0.01 (0.006) | <0.0001 | (Wilcoxon rank sum exact test) |
| wavelet.HHH_glcm_ClusterProminence | Mean (std) | 0.1 (0.1) | 0.04 (0.03) | <0.0001 | (Wilcoxon rank sum exact test) |
| wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 0.2 (0.1) | 0.4 (0.1) | <0.0001 | (Two Sample t-test) |
| wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis | Mean (std) | 0.3 (0.1) | 0.8 (0.1) | <0.0001 | (Welch Two Sample t-test) |
| wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 0.1 (0.1) | 0.03 (0.02) | <0.0001 | (Wilcoxon rank sum exact test) |
| wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis | Mean (std) | 0.3 (0.2) | 0.04 (0.02) | <0.0001 | (Wilcoxon rank sum exact test) |
| log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 0.2 (0.1) | 0.5 (0.1) | <0.0001 | (Two Sample t-test) |
| wavelet.LHH_glszm_GrayLevelNonUniformity | Mean (std) | 0.1 (0.04) | 0.4 (0.1) | <0.0001 | (Wilcoxon rank sum exact test) |
| log.sigma.5.0.mm.3D_firstorder_Kurtosis | Mean (std) | 0.04 (0.04) | 0.2 (0.1) | <0.0001 | (Wilcoxon rank sum exact test) |
| wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 0.2 (0.1) | 0.1 (0.02) | <0.0001 | (Wilcoxon rank sum exact test) |
| wavelet.HLH_glcm_DifferenceAverage | Mean (std) | 0.5 (0.2) | 0.3 (0.04) | <0.0001 | (Wilcoxon rank sum exact test) |
| wavelet.LHL_gldm_DependenceVariance | Mean (std) | 0.2 (0.2) | 0.4 (0.2) | <0.0001 | (Wilcoxon rank sum exact test) |
| clinical.T.Stage | 1 | 9 (100.00%) | 0 (0%) | <0.0001 | (Fisher’s Exact Test for Count Data) |
| clinical.T.Stage | 2 | 2 (10.53%) | 17 (89.47%) | <0.0001 | (Fisher’s Exact Test for Count Data) |
| clinical.T.Stage | 3 | 4 (44.44%) | 5 (55.56%) | <0.0001 | (Fisher’s Exact Test for Count Data) |
| clinical.T.Stage | 4 | 5 (31.25%) | 11 (68.75%) | <0.0001 | (Fisher’s Exact Test for Count Data) |
| wavelet.LHH_glcm_Imc1 | Mean (std) | 0.5 (0.2) | 0.7 (0.1) | 0.0001 | (Welch Two Sample t-test) |
| log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 0.1 (0.03) | 0.03 (0.03) | 0.0003 | (Wilcoxon rank sum exact test) |
| wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis | Mean (std) | 0.1 (0.1) | 0.1 (0.03) | 0.0005 | (Welch Two Sample t-test) |
| wavelet.HHH_glcm_Imc1 | Mean (std) | 0.7 (0.2) | 0.6 (0.2) | 0.0029 | (Wilcoxon rank sum exact test) |
| wavelet.HHH_glrlm_GrayLevelVariance | Mean (std) | 0.3 (0.3) | 0.1 (0.02) | 0.0037 | (Wilcoxon rank sum exact test) |
| log.sigma.2.0.mm.3D_glcm_Autocorrelation | Mean (std) | 0.2 (0.1) | 0.3 (0.1) | 0.0571 | (Wilcoxon rank sum exact test) |
| log.sigma.5.0.mm.3D_firstorder_90Percentile | Mean (std) | 0.8 (0.2) | 0.9 (0.03) | 0.0796 | (Wilcoxon rank sum exact test) |
| wavelet.LLL_glcm_ClusterProminence | Mean (std) | 0.7 (0.2) | 0.7 (0.1) | 0.1009 | (Wilcoxon rank sum exact test) |
| wavelet.HHH_glszm_GrayLevelNonUniformityNormalized | Mean (std) | 0.3 (0.2) | 0.2 (0.1) | 0.1145 | (Wilcoxon rank sum test) |
| wavelet.HHH_firstorder_RootMeanSquared | Mean (std) | 0.7 (0.2) | 0.8 (0.03) | 0.1457 | (Wilcoxon rank sum exact test) |
| Clinical.N.Stage | 0 | 7 (26.92%) | 19 (73.08%) | 0.2409 | (Fisher’s Exact Test for Count Data) |
| Clinical.N.Stage | 1_2 | 7 (43.75%) | 9 (56.25%) | 0.2409 | (Fisher’s Exact Test for Count Data) |
| Clinical.N.Stage | 3 | 6 (54.55%) | 5 (45.45%) | 0.2409 | (Fisher’s Exact Test for Count Data) |
| log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized | Mean (std) | 0.5 (0.2) | 0.4 (0.2) | 0.2533 | (Two Sample t-test) |
| Manufacturer | CMS Inc. | 6 (30.00%) | 14 (70.00%) | 0.3657 | (Pearson’s Chi-squared test) |
| Manufacturer | SIEMENS | 14 (42.42%) | 19 (57.58%) | 0.3657 | (Pearson’s Chi-squared test) |
| wavelet.LHH_firstorder_Skewness | Mean (std) | 0.5 (0.2) | 0.5 (0.05) | 0.3768 | (Wilcoxon rank sum exact test) |
| Overall.Stage | I | 4 (33.33%) | 8 (66.67%) | 0.3921 | (Fisher’s Exact Test for Count Data) |
| Overall.Stage | II | 1 (16.67%) | 5 (83.33%) | 0.3921 | (Fisher’s Exact Test for Count Data) |
| Overall.Stage | IIIa | 6 (60.00%) | 4 (40.00%) | 0.3921 | (Fisher’s Exact Test for Count Data) |
| Overall.Stage | IIIb | 9 (36.00%) | 16 (64.00%) | 0.3921 | (Fisher’s Exact Test for Count Data) |
| original_glcm_Imc1 | Mean (std) | 0.6 (0.1) | 0.6 (0.1) | 0.4544 | (Two Sample t-test) |
| age | Mean (std) | 0.6 (0.2) | 0.6 (0.2) | 0.4649 | (Two Sample t-test) |
| gender | female | 7 (43.75%) | 9 (56.25%) | 0.5525 | (Pearson’s Chi-squared test) |
| gender | male | 13 (35.14%) | 24 (64.86%) | 0.5525 | (Pearson’s Chi-squared test) |
| Histology | adenocarcinoma | 1 (25.00%) | 3 (75.00%) | 0.9506 | (Fisher’s Exact Test for Count Data) |
| Histology | large cell | 7 (43.75%) | 9 (56.25%) | 0.9506 | (Fisher’s Exact Test for Count Data) |
| Histology | nos | 5 (38.46%) | 8 (61.54%) | 0.9506 | (Fisher’s Exact Test for Count Data) |
| Histology | squamous cell carcinoma | 7 (35.00%) | 13 (65.00%) | 0.9506 | (Fisher’s Exact Test for Count Data) |
table24 %>% save_kable("tables/table24.png")
I generate the model:
# I adjust VarSelCluster model without variables selection for this second
# approach and using BIC criteria to estimate the optimal number of clusters
set.seed(12345)
res_varsel <- VarSelCluster(x = lung_data_filtered_scaled_radiomics,
gvals = 2:10,
nbcores = 4,
vbleSelec = FALSE,
crit.varsel = "BIC")
# I check the resulting model summary
summary(res_varsel)
## Model:
## Number of components: 10
## Model selection has been performed according to the BIC criterion
res_varsel@model@names.relevant
## [1] "age"
## [2] "original_shape_VoxelVolume"
## [3] "wavelet.HHL_glcm_ClusterProminence"
## [4] "log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis"
## [5] "log.sigma.2.0.mm.3D_glcm_Autocorrelation"
## [6] "log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis"
## [7] "wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis"
## [8] "wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis"
## [9] "wavelet.HHH_glszm_GrayLevelNonUniformity"
## [10] "wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis"
## [11] "wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis"
## [12] "wavelet.HHH_glcm_ClusterProminence"
## [13] "wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis"
## [14] "wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis"
## [15] "wavelet.LLL_glcm_ClusterProminence"
## [16] "wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis"
## [17] "wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis"
## [18] "wavelet.HHH_glrlm_GrayLevelVariance"
## [19] "log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized"
## [20] "wavelet.HHH_glcm_Imc1"
## [21] "original_glcm_Imc1"
## [22] "log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis"
## [23] "wavelet.LHH_glszm_GrayLevelNonUniformity"
## [24] "wavelet.LHH_firstorder_Skewness"
## [25] "wavelet.HHH_glszm_GrayLevelNonUniformityNormalized"
## [26] "wavelet.LHH_glcm_Imc1"
## [27] "log.sigma.5.0.mm.3D_firstorder_Kurtosis"
## [28] "log.sigma.5.0.mm.3D_firstorder_90Percentile"
## [29] "wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis"
## [30] "wavelet.HLH_glcm_DifferenceAverage"
## [31] "wavelet.HHH_firstorder_RootMeanSquared"
## [32] "wavelet.LHL_gldm_DependenceVariance"
## [33] "gender"
res_varsel@model@names.irrelevant
## character(0)
# I check the discriminative power of the variables
tiff(filename = "figures/Fig28.tiff", width = 250, height = 150, units = "mm", res = 300)
plot(res_varsel)
fig <- dev.off()
plot(res_varsel)
# I check fit for main discriminative variables
tiff(filename = "figures/Fig28.1.tiff", width = 200, height = 800, units = "mm", res = 300)
plot(res_varsel, y="original_shape_VoxelVolume", type="cdf")
plot(res_varsel, y="wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis", type="cdf")
plot(res_varsel, y="wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis", type="cdf")
fig <- dev.off()
plot(res_varsel, y="original_shape_VoxelVolume", type="cdf")
plot(res_varsel, y="wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis", type="cdf")
plot(res_varsel, y="wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis", type="cdf")
# I build q data frame including partitions, variables used in the model and other variables of interest
lung_data_res_varsel <- lung_data_filtered_scaled_radiomics %>%
cbind(lung_data_no_out %>% select(Histology,
Survival.time,
deadstatus.event,
Manufacturer,
Overall.Stage,
clinical.T.Stage,
Clinical.N.Stage),
partition = as.factor(res_varsel@partitions@zMAP))
# I build a data frame with principal components for radiomic variables to ease visualisation of results
pc_radiomics <- prcomp(lung_data_filtered_scaled_radiomics %>% keep(is.numeric), scale = TRUE)
pc_rad <- pc_radiomics$x[,1:3]
pc_df <- as.data.frame(pc_rad %>% cbind(partition = as.factor(res_varsel@partitions@zMAP)))
# I generate a representation of the clusters using the main discriminative variables to define the axes
# (screencapture saved as figure 29)
plot_ly(pc_df, x=~PC1, y=~PC2,
z=~PC3, color=~as.factor(partition)) %>%
add_markers(size=1.5)%>%
layout(
scene = list(
xaxis = list(size = 0.5),
yaxis = list(size = 0.5),
zaxis = list(size = 0.5)))
# I calculate a distance metric to be able to measure Silhouette Coefficient:
gower_dist <- daisy(lung_data_filtered_scaled_radiomics,
metric = "gower")
sil <- silhouette(res_varsel@partitions@zMAP, gower_dist)
tiff(filename = "figures/Fig29.1.tiff", width = 200, height = 150, units = "mm", res = 300)
plot(sil)
fig <- dev.off()
plot(sil)
# I display a summary of the probabilities of missclassification for each cluster
tiff(filename = "figures/Fig30.tiff", width = 400, height = 150, units = "mm", res = 300)
plot(res_varsel, type="probs-class")
fig <- dev.off()
plot(res_varsel, type="probs-class")
(crosstab<- crosstable(lung_data_res_varsel %>% dplyr::select(.,-c(deadstatus.event,Survival.time)), showNA = FALSE,
by = partition) %>% flextable()%>% autofit())
.id | label | variable | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
gender | gender | female | 14 (11.86%) | 12 (10.17%) | 16 (13.56%) | 9 (7.63%) | 6 (5.08%) | 12 (10.17%) | 15 (12.71%) | 5 (4.24%) | 11 (9.32%) | 18 (15.25%) |
gender | gender | male | 30 (11.58%) | 22 (8.49%) | 21 (8.11%) | 23 (8.88%) | 22 (8.49%) | 38 (14.67%) | 29 (11.20%) | 24 (9.27%) | 26 (10.04%) | 24 (9.27%) |
age | age | Min / Max | 0.2 / 0.9 | 0 / 0.9 | 0.3 / 0.8 | 0.3 / 0.9 | 0.3 / 0.8 | 0.2 / 0.9 | 0.2 / 0.9 | 0.3 / 0.9 | 0.2 / 1.0 | 0.2 / 0.9 |
age | age | Med [IQR] | 0.6 [0.5;0.7] | 0.6 [0.5;0.7] | 0.6 [0.5;0.7] | 0.5 [0.4;0.7] | 0.7 [0.6;0.8] | 0.5 [0.4;0.7] | 0.6 [0.5;0.7] | 0.7 [0.6;0.8] | 0.7 [0.5;0.8] | 0.6 [0.5;0.7] |
age | age | Mean (std) | 0.6 (0.2) | 0.6 (0.2) | 0.6 (0.2) | 0.6 (0.2) | 0.6 (0.1) | 0.5 (0.2) | 0.6 (0.2) | 0.7 (0.1) | 0.6 (0.2) | 0.6 (0.2) |
age | age | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
original_shape_VoxelVolume | original_shape_VoxelVolume | Min / Max | 0.4 / 0.7 | 0 / 0.3 | 0.1 / 0.6 | 0.4 / 1.0 | 0.3 / 0.9 | 0.6 / 1.0 | 0.5 / 0.9 | 0.7 / 1.0 | 0.4 / 0.8 | 0.2 / 0.5 |
original_shape_VoxelVolume | original_shape_VoxelVolume | Med [IQR] | 0.6 [0.5;0.6] | 0.2 [0.1;0.2] | 0.4 [0.3;0.5] | 0.7 [0.6;0.8] | 0.7 [0.6;0.8] | 0.8 [0.7;0.8] | 0.7 [0.6;0.8] | 0.8 [0.8;0.9] | 0.6 [0.6;0.7] | 0.4 [0.3;0.4] |
original_shape_VoxelVolume | original_shape_VoxelVolume | Mean (std) | 0.6 (0.1) | 0.2 (0.1) | 0.4 (0.1) | 0.7 (0.1) | 0.7 (0.2) | 0.8 (0.1) | 0.7 (0.1) | 0.9 (0.1) | 0.6 (0.1) | 0.4 (0.1) |
original_shape_VoxelVolume | original_shape_VoxelVolume | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
wavelet.HHL_glcm_ClusterProminence | wavelet.HHL_glcm_ClusterProminence | Min / Max | 0.002 / 0.1 | 0.001 / 0.2 | 0.1 / 0.5 | 0 / 0.1 | 0.02 / 1.0 | 0.002 / 0.1 | 0.1 / 0.4 | 0.005 / 0.1 | 0.02 / 0.2 | 0.006 / 0.1 |
wavelet.HHL_glcm_ClusterProminence | wavelet.HHL_glcm_ClusterProminence | Med [IQR] | 0.02 [0.01;0.03] | 0.1 [0.02;0.1] | 0.2 [0.1;0.4] | 0.01 [0.007;0.03] | 0.3 [0.1;0.4] | 0.02 [0.01;0.03] | 0.2 [0.1;0.2] | 0.1 [0.1;0.1] | 0.1 [0.1;0.1] | 0.05 [0.03;0.1] |
wavelet.HHL_glcm_ClusterProminence | wavelet.HHL_glcm_ClusterProminence | Mean (std) | 0.03 (0.02) | 0.1 (0.05) | 0.2 (0.1) | 0.02 (0.02) | 0.3 (0.2) | 0.02 (0.02) | 0.2 (0.1) | 0.1 (0.04) | 0.1 (0.05) | 0.1 (0.04) |
wavelet.HHL_glcm_ClusterProminence | wavelet.HHL_glcm_ClusterProminence | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis | log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis | Min / Max | 0.04 / 0.3 | 8e-05 / 0.1 | 0 / 0.2 | 0.2 / 1.0 | 0.005 / 0.6 | 0.1 / 0.9 | 0.04 / 0.5 | 0.2 / 0.9 | 0.03 / 0.6 | 3e-04 / 0.2 |
log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis | log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis | Med [IQR] | 0.2 [0.1;0.2] | 0.006 [0.002;0.02] | 0.04 [0.003;0.1] | 0.4 [0.3;0.6] | 0.2 [0.1;0.4] | 0.4 [0.3;0.5] | 0.2 [0.2;0.3] | 0.5 [0.4;0.6] | 0.2 [0.2;0.3] | 0.05 [0.02;0.1] |
log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis | log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis | Mean (std) | 0.2 (0.1) | 0.01 (0.02) | 0.05 (0.05) | 0.5 (0.2) | 0.3 (0.2) | 0.4 (0.2) | 0.2 (0.1) | 0.5 (0.2) | 0.3 (0.1) | 0.1 (0.05) |
log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis | log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
log.sigma.2.0.mm.3D_glcm_Autocorrelation | log.sigma.2.0.mm.3D_glcm_Autocorrelation | Min / Max | 0.1 / 0.4 | 0.01 / 0.3 | 0.1 / 0.5 | 0 / 0.2 | 0.1 / 1.0 | 0.1 / 0.4 | 0.2 / 0.5 | 0.1 / 0.5 | 0.1 / 0.3 | 0.03 / 0.3 |
log.sigma.2.0.mm.3D_glcm_Autocorrelation | log.sigma.2.0.mm.3D_glcm_Autocorrelation | Med [IQR] | 0.2 [0.1;0.2] | 0.1 [0.1;0.2] | 0.2 [0.1;0.2] | 0.1 [0.1;0.1] | 0.4 [0.3;0.6] | 0.2 [0.2;0.2] | 0.3 [0.2;0.3] | 0.2 [0.2;0.2] | 0.2 [0.1;0.2] | 0.1 [0.1;0.2] |
log.sigma.2.0.mm.3D_glcm_Autocorrelation | log.sigma.2.0.mm.3D_glcm_Autocorrelation | Mean (std) | 0.2 (0.1) | 0.1 (0.1) | 0.2 (0.1) | 0.1 (0.1) | 0.5 (0.2) | 0.2 (0.1) | 0.3 (0.1) | 0.2 (0.1) | 0.2 (0.1) | 0.1 (0.1) |
log.sigma.2.0.mm.3D_glcm_Autocorrelation | log.sigma.2.0.mm.3D_glcm_Autocorrelation | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis | log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis | Min / Max | 0.009 / 0.1 | 0.02 / 1.0 | 0.01 / 0.1 | 0.007 / 0.4 | 0 / 0.1 | 0.002 / 0.1 | 0.007 / 0.2 | 0.008 / 0.1 | 0.003 / 0.2 | 0.009 / 0.3 |
log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis | log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis | Med [IQR] | 0.03 [0.02;0.04] | 0.1 [0.04;0.1] | 0.03 [0.02;0.05] | 0.1 [0.03;0.1] | 0.02 [0.01;0.04] | 0.03 [0.02;0.04] | 0.03 [0.02;0.04] | 0.03 [0.02;0.04] | 0.03 [0.02;0.04] | 0.03 [0.02;0.04] |
log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis | log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 0.03 (0.02) | 0.2 (0.2) | 0.04 (0.03) | 0.1 (0.1) | 0.02 (0.02) | 0.03 (0.02) | 0.04 (0.03) | 0.03 (0.01) | 0.04 (0.04) | 0.04 (0.1) |
log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis | log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis | wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis | Min / Max | 0.4 / 0.7 | 0 / 0.4 | 0.1 / 0.5 | 0.4 / 0.8 | 0.4 / 0.9 | 0.6 / 1.0 | 0.5 / 0.8 | 0.6 / 0.9 | 0.5 / 0.7 | 0.2 / 0.5 |
wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis | wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis | Med [IQR] | 0.5 [0.5;0.6] | 0.2 [0.1;0.3] | 0.3 [0.2;0.4] | 0.6 [0.6;0.7] | 0.7 [0.6;0.8] | 0.8 [0.7;0.8] | 0.7 [0.6;0.7] | 0.8 [0.7;0.8] | 0.6 [0.5;0.6] | 0.4 [0.3;0.4] |
wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis | wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis | Mean (std) | 0.5 (0.1) | 0.2 (0.1) | 0.3 (0.1) | 0.6 (0.1) | 0.7 (0.1) | 0.8 (0.1) | 0.7 (0.1) | 0.8 (0.1) | 0.6 (0.1) | 0.4 (0.1) |
wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis | wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis | wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis | Min / Max | 0.002 / 0.03 | 0 / 0.1 | 0.02 / 0.2 | 0.001 / 0.05 | 0.02 / 1.0 | 0.002 / 0.04 | 0.03 / 0.2 | 0.01 / 0.1 | 0.02 / 0.1 | 0.003 / 0.1 |
wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis | wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis | Med [IQR] | 0.009 [0.007;0.01] | 0.01 [0.008;0.03] | 0.1 [0.1;0.1] | 0.01 [0.007;0.02] | 0.1 [0.1;0.1] | 0.01 [0.008;0.02] | 0.1 [0.1;0.1] | 0.1 [0.05;0.1] | 0.05 [0.03;0.1] | 0.02 [0.01;0.03] |
wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis | wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis | Mean (std) | 0.01 (0.006) | 0.02 (0.02) | 0.1 (0.05) | 0.01 (0.01) | 0.1 (0.2) | 0.01 (0.007) | 0.1 (0.03) | 0.1 (0.03) | 0.05 (0.02) | 0.02 (0.01) |
wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis | wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
wavelet.HHH_glszm_GrayLevelNonUniformity | wavelet.HHH_glszm_GrayLevelNonUniformity | Min / Max | 0 / 0.1 | 0 / 0.03 | 0 / 0.2 | 0 / 0.1 | 0.02 / 1.0 | 0.007 / 0.1 | 0.004 / 0.4 | 0.02 / 0.3 | 0 / 0.1 | 0 / 0.05 |
wavelet.HHH_glszm_GrayLevelNonUniformity | wavelet.HHH_glszm_GrayLevelNonUniformity | Med [IQR] | 0.02 [0.02;0.03] | 0.007 [0;0.01] | 0.01 [0.006;0.03] | 0.02 [0.007;0.03] | 0.2 [0.1;0.3] | 0.04 [0.03;0.1] | 0.1 [0.1;0.2] | 0.1 [0.04;0.1] | 0.01 [0.007;0.02] | 0.007 [0.007;0.02] |
wavelet.HHH_glszm_GrayLevelNonUniformity | wavelet.HHH_glszm_GrayLevelNonUniformity | Mean (std) | 0.02 (0.01) | 0.007 (0.008) | 0.02 (0.03) | 0.02 (0.02) | 0.3 (0.3) | 0.04 (0.03) | 0.1 (0.1) | 0.1 (0.1) | 0.02 (0.02) | 0.01 (0.01) |
wavelet.HHH_glszm_GrayLevelNonUniformity | wavelet.HHH_glszm_GrayLevelNonUniformity | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis | wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis | Min / Max | 0.1 / 0.6 | 0 / 0.2 | 0.1 / 0.4 | 0.1 / 0.6 | 0.2 / 1.0 | 0.2 / 0.7 | 0.2 / 0.7 | 0.2 / 0.6 | 0.1 / 0.6 | 0.1 / 0.3 |
wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis | wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis | Med [IQR] | 0.3 [0.2;0.3] | 0.1 [0.1;0.1] | 0.2 [0.2;0.3] | 0.2 [0.2;0.3] | 0.6 [0.4;0.7] | 0.4 [0.3;0.5] | 0.4 [0.3;0.5] | 0.4 [0.3;0.5] | 0.3 [0.3;0.3] | 0.2 [0.1;0.2] |
wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis | wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 0.3 (0.1) | 0.1 (0.1) | 0.2 (0.1) | 0.2 (0.1) | 0.6 (0.2) | 0.4 (0.1) | 0.4 (0.1) | 0.4 (0.1) | 0.3 (0.1) | 0.2 (0.1) |
wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis | wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis | wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis | Min / Max | 0.01 / 0.1 | 0.1 / 1.0 | 0.03 / 0.2 | 0.007 / 0.1 | 3e-04 / 0.1 | 0.002 / 0.04 | 0.003 / 0.04 | 0 / 0.03 | 0.009 / 0.1 | 0.03 / 0.2 |
wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis | wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis | Med [IQR] | 0.1 [0.04;0.1] | 0.2 [0.1;0.3] | 0.1 [0.1;0.1] | 0.03 [0.02;0.05] | 0.01 [0.003;0.02] | 0.01 [0.008;0.02] | 0.02 [0.01;0.02] | 0.01 [0.006;0.02] | 0.03 [0.02;0.04] | 0.1 [0.1;0.1] |
wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis | wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis | Mean (std) | 0.1 (0.03) | 0.3 (0.2) | 0.1 (0.05) | 0.04 (0.02) | 0.02 (0.02) | 0.02 (0.009) | 0.02 (0.007) | 0.01 (0.009) | 0.03 (0.01) | 0.1 (0.04) |
wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis | wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
wavelet.HHH_glcm_ClusterProminence | wavelet.HHH_glcm_ClusterProminence | Min / Max | 0.05 / 0.1 | 0.04 / 0.2 | 0.03 / 0.2 | 0 / 0.1 | 0.01 / 1.0 | 0.02 / 0.1 | 0.02 / 0.1 | 0.003 / 0.04 | 0.01 / 0.1 | 0.008 / 0.2 |
wavelet.HHH_glcm_ClusterProminence | wavelet.HHH_glcm_ClusterProminence | Med [IQR] | 0.1 [0.1;0.1] | 0.1 [0.1;0.1] | 0.1 [0.1;0.1] | 0.05 [0.02;0.1] | 0.1 [0.1;0.2] | 0.1 [0.1;0.1] | 0.1 [0.04;0.1] | 0.02 [0.01;0.03] | 0.03 [0.02;0.05] | 0.1 [0.1;0.1] |
wavelet.HHH_glcm_ClusterProminence | wavelet.HHH_glcm_ClusterProminence | Mean (std) | 0.1 (0.02) | 0.1 (0.03) | 0.1 (0.05) | 0.04 (0.02) | 0.1 (0.2) | 0.1 (0.02) | 0.1 (0.02) | 0.02 (0.01) | 0.04 (0.02) | 0.1 (0.04) |
wavelet.HHH_glcm_ClusterProminence | wavelet.HHH_glcm_ClusterProminence | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis | wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis | Min / Max | 0.1 / 0.4 | 0 / 0.2 | 0.04 / 0.3 | 0.1 / 0.3 | 0.2 / 1.0 | 0.2 / 0.5 | 0.2 / 0.5 | 0.2 / 0.7 | 0.1 / 0.4 | 0.03 / 0.3 |
wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis | wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis | Med [IQR] | 0.3 [0.2;0.3] | 0.1 [0.03;0.1] | 0.2 [0.1;0.2] | 0.3 [0.2;0.3] | 0.5 [0.3;0.7] | 0.4 [0.3;0.4] | 0.4 [0.3;0.4] | 0.4 [0.3;0.5] | 0.3 [0.2;0.3] | 0.1 [0.1;0.2] |
wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis | wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 0.2 (0.1) | 0.1 (0.1) | 0.2 (0.1) | 0.2 (0.05) | 0.5 (0.3) | 0.4 (0.1) | 0.3 (0.1) | 0.4 (0.1) | 0.3 (0.1) | 0.1 (0.1) |
wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis | wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis | wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis | Min / Max | 0.4 / 0.7 | 0 / 0.4 | 0.1 / 0.6 | 0.4 / 1.0 | 0.3 / 0.9 | 0.6 / 1.0 | 0.5 / 0.8 | 0.7 / 1.0 | 0.5 / 0.8 | 0.2 / 0.5 |
wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis | wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis | Med [IQR] | 0.6 [0.5;0.6] | 0.2 [0.2;0.3] | 0.4 [0.3;0.5] | 0.7 [0.6;0.8] | 0.7 [0.5;0.7] | 0.8 [0.7;0.8] | 0.7 [0.6;0.7] | 0.8 [0.8;0.9] | 0.7 [0.6;0.7] | 0.4 [0.4;0.5] |
wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis | wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis | Mean (std) | 0.6 (0.1) | 0.2 (0.1) | 0.4 (0.1) | 0.7 (0.1) | 0.6 (0.1) | 0.8 (0.1) | 0.7 (0.1) | 0.8 (0.1) | 0.7 (0.1) | 0.4 (0.1) |
wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis | wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
wavelet.LLL_glcm_ClusterProminence | wavelet.LLL_glcm_ClusterProminence | Min / Max | 0.3 / 0.7 | 0.3 / 0.9 | 0.5 / 0.9 | 0 / 0.6 | 0.2 / 1.0 | 0.1 / 0.9 | 0.5 / 0.9 | 0.2 / 0.8 | 0.4 / 0.8 | 0.2 / 0.8 |
wavelet.LLL_glcm_ClusterProminence | wavelet.LLL_glcm_ClusterProminence | Med [IQR] | 0.6 [0.5;0.7] | 0.6 [0.5;0.7] | 0.7 [0.6;0.8] | 0.4 [0.3;0.5] | 0.7 [0.6;0.8] | 0.5 [0.4;0.6] | 0.7 [0.6;0.8] | 0.5 [0.5;0.6] | 0.6 [0.5;0.7] | 0.6 [0.5;0.7] |
wavelet.LLL_glcm_ClusterProminence | wavelet.LLL_glcm_ClusterProminence | Mean (std) | 0.6 (0.1) | 0.6 (0.1) | 0.7 (0.1) | 0.4 (0.2) | 0.7 (0.2) | 0.5 (0.2) | 0.7 (0.1) | 0.5 (0.1) | 0.6 (0.1) | 0.6 (0.1) |
wavelet.LLL_glcm_ClusterProminence | wavelet.LLL_glcm_ClusterProminence | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis | wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis | Min / Max | 0.05 / 0.3 | 0.1 / 1.0 | 0.03 / 0.2 | 0.04 / 0.3 | 0 / 0.1 | 0.03 / 0.2 | 0.01 / 0.1 | 0.008 / 0.1 | 0.02 / 0.1 | 0.1 / 0.3 |
wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis | wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis | Med [IQR] | 0.1 [0.1;0.1] | 0.3 [0.2;0.3] | 0.1 [0.1;0.1] | 0.1 [0.1;0.2] | 0.02 [0.01;0.03] | 0.1 [0.05;0.1] | 0.03 [0.02;0.04] | 0.03 [0.02;0.04] | 0.1 [0.04;0.1] | 0.2 [0.1;0.2] |
wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis | wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 0.1 (0.1) | 0.3 (0.2) | 0.1 (0.04) | 0.1 (0.1) | 0.03 (0.02) | 0.1 (0.03) | 0.03 (0.01) | 0.03 (0.02) | 0.1 (0.02) | 0.2 (0.1) |
wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis | wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis | wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis | Min / Max | 0.04 / 0.3 | 0.2 / 1.0 | 0.1 / 0.8 | 0.02 / 0.3 | 0 / 0.2 | 0.01 / 0.1 | 0.02 / 0.1 | 0.01 / 0.2 | 0.03 / 0.2 | 0.1 / 0.5 |
wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis | wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis | Med [IQR] | 0.1 [0.1;0.2] | 0.6 [0.4;0.7] | 0.2 [0.2;0.3] | 0.1 [0.1;0.2] | 0.04 [0.02;0.1] | 0.1 [0.04;0.1] | 0.1 [0.04;0.1] | 0.05 [0.04;0.1] | 0.1 [0.1;0.1] | 0.3 [0.2;0.3] |
wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis | wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis | Mean (std) | 0.1 (0.1) | 0.6 (0.2) | 0.2 (0.1) | 0.1 (0.1) | 0.1 (0.05) | 0.1 (0.03) | 0.1 (0.03) | 0.1 (0.04) | 0.1 (0.04) | 0.3 (0.1) |
wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis | wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
wavelet.HHH_glrlm_GrayLevelVariance | wavelet.HHH_glrlm_GrayLevelVariance | Min / Max | 0.02 / 0.02 | 0 / 0.2 | 0.01 / 0.7 | 0.02 / 0.02 | 0.03 / 1.0 | 0.02 / 0.04 | 0.03 / 0.2 | 0.02 / 0.1 | 0.02 / 0.1 | 0.02 / 0.02 |
wavelet.HHH_glrlm_GrayLevelVariance | wavelet.HHH_glrlm_GrayLevelVariance | Med [IQR] | 0.02 [0.02;0.02] | 0.02 [0.02;0.02] | 0.1 [0.05;0.2] | 0.02 [0.02;0.02] | 0.2 [0.1;0.3] | 0.02 [0.02;0.02] | 0.1 [0.1;0.1] | 0.04 [0.03;0.1] | 0.03 [0.03;0.05] | 0.02 [0.02;0.02] |
wavelet.HHH_glrlm_GrayLevelVariance | wavelet.HHH_glrlm_GrayLevelVariance | Mean (std) | 0.02 (1e-04) | 0.03 (0.04) | 0.1 (0.1) | 0.02 (4e-05) | 0.3 (0.2) | 0.03 (0.002) | 0.1 (0.03) | 0.04 (0.01) | 0.04 (0.02) | 0.02 (4e-04) |
wavelet.HHH_glrlm_GrayLevelVariance | wavelet.HHH_glrlm_GrayLevelVariance | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized | log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized | Min / Max | 0.2 / 0.7 | 0.1 / 0.9 | 0.2 / 1.0 | 0.02 / 0.6 | 0 / 0.7 | 0.2 / 0.8 | 0.1 / 0.7 | 0.1 / 0.6 | 0.1 / 0.7 | 0.05 / 0.7 |
log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized | log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized | Med [IQR] | 0.4 [0.4;0.5] | 0.5 [0.3;0.6] | 0.5 [0.3;0.6] | 0.4 [0.3;0.4] | 0.4 [0.3;0.6] | 0.5 [0.4;0.6] | 0.5 [0.4;0.5] | 0.4 [0.3;0.5] | 0.4 [0.3;0.5] | 0.5 [0.4;0.6] |
log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized | log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized | Mean (std) | 0.5 (0.1) | 0.5 (0.2) | 0.5 (0.2) | 0.3 (0.1) | 0.4 (0.2) | 0.5 (0.1) | 0.5 (0.1) | 0.4 (0.1) | 0.4 (0.1) | 0.4 (0.2) |
log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized | log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
wavelet.HHH_glcm_Imc1 | wavelet.HHH_glcm_Imc1 | Min / Max | 0.6 / 0.9 | 0.1 / 1.0 | 0.2 / 1.0 | 0.3 / 1.0 | 0 / 0.9 | 0.5 / 0.9 | 0.4 / 0.9 | 0.1 / 0.7 | 0.2 / 0.9 | 0.3 / 1.0 |
wavelet.HHH_glcm_Imc1 | wavelet.HHH_glcm_Imc1 | Med [IQR] | 0.9 [0.8;0.9] | 0.8 [0.7;0.9] | 0.8 [0.7;0.8] | 0.7 [0.5;0.9] | 0.7 [0.6;0.8] | 0.9 [0.8;0.9] | 0.7 [0.6;0.8] | 0.6 [0.5;0.6] | 0.6 [0.5;0.7] | 0.8 [0.7;0.9] |
wavelet.HHH_glcm_Imc1 | wavelet.HHH_glcm_Imc1 | Mean (std) | 0.8 (0.1) | 0.7 (0.2) | 0.8 (0.1) | 0.7 (0.2) | 0.7 (0.2) | 0.8 (0.1) | 0.7 (0.1) | 0.5 (0.1) | 0.6 (0.2) | 0.8 (0.1) |
wavelet.HHH_glcm_Imc1 | wavelet.HHH_glcm_Imc1 | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
original_glcm_Imc1 | original_glcm_Imc1 | Min / Max | 0.4 / 0.7 | 0 / 0.7 | 0.3 / 0.8 | 0.4 / 0.9 | 0.3 / 0.8 | 0.3 / 0.8 | 0.3 / 0.8 | 0.3 / 1.0 | 0.4 / 0.8 | 0.3 / 0.8 |
original_glcm_Imc1 | original_glcm_Imc1 | Med [IQR] | 0.6 [0.5;0.7] | 0.6 [0.5;0.6] | 0.7 [0.6;0.7] | 0.6 [0.6;0.7] | 0.5 [0.5;0.6] | 0.6 [0.5;0.7] | 0.6 [0.5;0.7] | 0.6 [0.5;0.8] | 0.6 [0.5;0.7] | 0.6 [0.6;0.7] |
original_glcm_Imc1 | original_glcm_Imc1 | Mean (std) | 0.6 (0.1) | 0.5 (0.1) | 0.6 (0.1) | 0.7 (0.1) | 0.5 (0.1) | 0.6 (0.1) | 0.6 (0.1) | 0.6 (0.2) | 0.6 (0.1) | 0.6 (0.1) |
original_glcm_Imc1 | original_glcm_Imc1 | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis | log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis | Min / Max | 0.1 / 0.5 | 0 / 0.3 | 0.04 / 0.4 | 0.2 / 0.5 | 0.2 / 1.0 | 0.2 / 0.7 | 0.2 / 0.7 | 0.3 / 0.8 | 0.2 / 0.5 | 0.03 / 0.3 |
log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis | log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis | Med [IQR] | 0.3 [0.2;0.3] | 0.1 [0.1;0.1] | 0.2 [0.1;0.2] | 0.3 [0.3;0.4] | 0.6 [0.3;0.8] | 0.5 [0.4;0.5] | 0.4 [0.4;0.5] | 0.5 [0.5;0.6] | 0.3 [0.2;0.3] | 0.1 [0.1;0.2] |
log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis | log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 0.3 (0.1) | 0.1 (0.1) | 0.2 (0.1) | 0.4 (0.1) | 0.6 (0.3) | 0.5 (0.1) | 0.4 (0.1) | 0.5 (0.1) | 0.3 (0.1) | 0.1 (0.1) |
log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis | log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
wavelet.LHH_glszm_GrayLevelNonUniformity | wavelet.LHH_glszm_GrayLevelNonUniformity | Min / Max | 0.02 / 0.2 | 0 / 0.1 | 0.02 / 0.2 | 0.006 / 0.3 | 0.02 / 0.8 | 0.1 / 0.6 | 0.1 / 1.0 | 0.1 / 0.8 | 0.02 / 0.5 | 0.005 / 0.1 |
wavelet.LHH_glszm_GrayLevelNonUniformity | wavelet.LHH_glszm_GrayLevelNonUniformity | Med [IQR] | 0.1 [0.1;0.2] | 0.02 [0.01;0.03] | 0.1 [0.1;0.1] | 0.1 [0.04;0.2] | 0.3 [0.2;0.5] | 0.2 [0.2;0.3] | 0.3 [0.2;0.4] | 0.4 [0.3;0.4] | 0.2 [0.1;0.2] | 0.1 [0.04;0.1] |
wavelet.LHH_glszm_GrayLevelNonUniformity | wavelet.LHH_glszm_GrayLevelNonUniformity | Mean (std) | 0.1 (0.1) | 0.02 (0.01) | 0.1 (0.1) | 0.1 (0.1) | 0.3 (0.2) | 0.2 (0.1) | 0.4 (0.2) | 0.4 (0.2) | 0.2 (0.1) | 0.1 (0.03) |
wavelet.LHH_glszm_GrayLevelNonUniformity | wavelet.LHH_glszm_GrayLevelNonUniformity | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
wavelet.LHH_firstorder_Skewness | wavelet.LHH_firstorder_Skewness | Min / Max | 0.4 / 0.6 | 0 / 0.7 | 0.4 / 0.6 | 0.2 / 0.6 | 0.4 / 1.0 | 0.3 / 0.9 | 0.4 / 0.6 | 0.4 / 0.7 | 0.3 / 0.6 | 0.3 / 0.8 |
wavelet.LHH_firstorder_Skewness | wavelet.LHH_firstorder_Skewness | Med [IQR] | 0.5 [0.4;0.5] | 0.5 [0.4;0.5] | 0.5 [0.4;0.5] | 0.4 [0.4;0.5] | 0.5 [0.4;0.6] | 0.5 [0.4;0.5] | 0.5 [0.4;0.5] | 0.5 [0.4;0.5] | 0.5 [0.4;0.5] | 0.5 [0.4;0.5] |
wavelet.LHH_firstorder_Skewness | wavelet.LHH_firstorder_Skewness | Mean (std) | 0.5 (0.1) | 0.5 (0.1) | 0.5 (0.1) | 0.4 (0.1) | 0.5 (0.2) | 0.5 (0.1) | 0.5 (0.05) | 0.5 (0.1) | 0.5 (0.1) | 0.5 (0.1) |
wavelet.LHH_firstorder_Skewness | wavelet.LHH_firstorder_Skewness | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
wavelet.HHH_glszm_GrayLevelNonUniformityNormalized | wavelet.HHH_glszm_GrayLevelNonUniformityNormalized | Min / Max | 0.5 / 0.9 | 0.2 / 0.9 | 0.04 / 0.6 | 0.5 / 0.9 | 0 / 0.5 | 0.05 / 1.0 | 0.05 / 0.7 | 0.1 / 0.8 | 0.1 / 0.8 | 0.5 / 0.9 |
wavelet.HHH_glszm_GrayLevelNonUniformityNormalized | wavelet.HHH_glszm_GrayLevelNonUniformityNormalized | Med [IQR] | 0.6 [0.5;0.7] | 0.5 [0.5;0.6] | 0.2 [0.1;0.3] | 0.6 [0.5;0.6] | 0.3 [0.2;0.4] | 0.6 [0.5;0.6] | 0.3 [0.2;0.3] | 0.3 [0.2;0.3] | 0.2 [0.1;0.3] | 0.6 [0.5;0.6] |
wavelet.HHH_glszm_GrayLevelNonUniformityNormalized | wavelet.HHH_glszm_GrayLevelNonUniformityNormalized | Mean (std) | 0.6 (0.1) | 0.5 (0.1) | 0.2 (0.2) | 0.6 (0.1) | 0.3 (0.1) | 0.6 (0.2) | 0.3 (0.1) | 0.3 (0.2) | 0.2 (0.2) | 0.6 (0.1) |
wavelet.HHH_glszm_GrayLevelNonUniformityNormalized | wavelet.HHH_glszm_GrayLevelNonUniformityNormalized | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
wavelet.LHH_glcm_Imc1 | wavelet.LHH_glcm_Imc1 | Min / Max | 0.3 / 0.9 | 0 / 0.9 | 0.3 / 0.8 | 0.5 / 1.0 | 0.1 / 1.0 | 0.5 / 0.9 | 0.5 / 0.9 | 0.7 / 0.9 | 0.6 / 0.9 | 0.3 / 0.9 |
wavelet.LHH_glcm_Imc1 | wavelet.LHH_glcm_Imc1 | Med [IQR] | 0.7 [0.6;0.7] | 0.6 [0.5;0.7] | 0.7 [0.5;0.7] | 0.8 [0.7;0.9] | 0.6 [0.5;0.7] | 0.8 [0.7;0.8] | 0.7 [0.6;0.7] | 0.8 [0.7;0.8] | 0.8 [0.7;0.8] | 0.7 [0.5;0.7] |
wavelet.LHH_glcm_Imc1 | wavelet.LHH_glcm_Imc1 | Mean (std) | 0.6 (0.1) | 0.5 (0.2) | 0.6 (0.2) | 0.8 (0.1) | 0.6 (0.2) | 0.7 (0.1) | 0.7 (0.1) | 0.8 (0.1) | 0.8 (0.1) | 0.6 (0.1) |
wavelet.LHH_glcm_Imc1 | wavelet.LHH_glcm_Imc1 | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
log.sigma.5.0.mm.3D_firstorder_Kurtosis | log.sigma.5.0.mm.3D_firstorder_Kurtosis | Min / Max | 0.02 / 0.2 | 0.004 / 0.1 | 0.004 / 0.2 | 0.02 / 0.4 | 0 / 0.4 | 0.03 / 0.4 | 0.03 / 0.5 | 0.05 / 1.0 | 0.04 / 0.3 | 0.006 / 0.4 |
log.sigma.5.0.mm.3D_firstorder_Kurtosis | log.sigma.5.0.mm.3D_firstorder_Kurtosis | Med [IQR] | 0.1 [0.04;0.1] | 0.04 [0.03;0.04] | 0.04 [0.03;0.1] | 0.1 [0.1;0.2] | 0.1 [0.1;0.2] | 0.1 [0.1;0.2] | 0.1 [0.1;0.2] | 0.2 [0.1;0.2] | 0.1 [0.1;0.1] | 0.03 [0.03;0.1] |
log.sigma.5.0.mm.3D_firstorder_Kurtosis | log.sigma.5.0.mm.3D_firstorder_Kurtosis | Mean (std) | 0.1 (0.04) | 0.04 (0.01) | 0.05 (0.03) | 0.1 (0.1) | 0.1 (0.1) | 0.1 (0.1) | 0.1 (0.1) | 0.2 (0.2) | 0.1 (0.1) | 0.05 (0.1) |
log.sigma.5.0.mm.3D_firstorder_Kurtosis | log.sigma.5.0.mm.3D_firstorder_Kurtosis | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
log.sigma.5.0.mm.3D_firstorder_90Percentile | log.sigma.5.0.mm.3D_firstorder_90Percentile | Min / Max | 0.6 / 0.9 | 0 / 1.0 | 0.6 / 1.0 | 0.8 / 0.9 | 0.8 / 1.0 | 0.8 / 0.9 | 0.8 / 1.0 | 0.8 / 0.9 | 0.7 / 0.9 | 0.6 / 0.9 |
log.sigma.5.0.mm.3D_firstorder_90Percentile | log.sigma.5.0.mm.3D_firstorder_90Percentile | Med [IQR] | 0.8 [0.8;0.9] | 0.8 [0.8;0.9] | 0.8 [0.8;0.9] | 0.9 [0.9;0.9] | 0.9 [0.9;0.9] | 0.9 [0.9;0.9] | 0.9 [0.9;0.9] | 0.9 [0.9;0.9] | 0.9 [0.8;0.9] | 0.8 [0.8;0.9] |
log.sigma.5.0.mm.3D_firstorder_90Percentile | log.sigma.5.0.mm.3D_firstorder_90Percentile | Mean (std) | 0.8 (0.04) | 0.8 (0.2) | 0.8 (0.1) | 0.9 (0.01) | 0.9 (0.03) | 0.9 (0.02) | 0.9 (0.03) | 0.9 (0.02) | 0.9 (0.04) | 0.8 (0.1) |
log.sigma.5.0.mm.3D_firstorder_90Percentile | log.sigma.5.0.mm.3D_firstorder_90Percentile | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis | wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis | Min / Max | 0.1 / 0.7 | 0.1 / 1.0 | 0.1 / 0.4 | 0.1 / 0.9 | 0 / 0.5 | 0.04 / 0.2 | 0.03 / 0.1 | 0.05 / 0.3 | 0.1 / 0.8 | 0.1 / 0.9 |
wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis | wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis | Med [IQR] | 0.2 [0.1;0.2] | 0.3 [0.2;0.6] | 0.2 [0.1;0.3] | 0.2 [0.2;0.3] | 0.05 [0.02;0.1] | 0.2 [0.1;0.2] | 0.1 [0.1;0.1] | 0.1 [0.1;0.1] | 0.2 [0.1;0.2] | 0.2 [0.2;0.4] |
wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis | wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 0.2 (0.2) | 0.4 (0.3) | 0.2 (0.1) | 0.3 (0.2) | 0.1 (0.1) | 0.2 (0.1) | 0.1 (0.02) | 0.1 (0.1) | 0.2 (0.1) | 0.3 (0.2) |
wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis | wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
wavelet.HLH_glcm_DifferenceAverage | wavelet.HLH_glcm_DifferenceAverage | Min / Max | 0.1 / 0.2 | 0 / 0.7 | 0.2 / 1.0 | 0.1 / 0.3 | 0.2 / 0.7 | 0.1 / 0.3 | 0.2 / 0.5 | 0.2 / 0.4 | 0.2 / 0.4 | 0.02 / 0.4 |
wavelet.HLH_glcm_DifferenceAverage | wavelet.HLH_glcm_DifferenceAverage | Med [IQR] | 0.1 [0.1;0.2] | 0.2 [0.1;0.3] | 0.3 [0.3;0.6] | 0.2 [0.2;0.2] | 0.3 [0.3;0.4] | 0.2 [0.2;0.2] | 0.3 [0.3;0.4] | 0.3 [0.3;0.3] | 0.3 [0.2;0.3] | 0.2 [0.1;0.2] |
wavelet.HLH_glcm_DifferenceAverage | wavelet.HLH_glcm_DifferenceAverage | Mean (std) | 0.1 (0.04) | 0.2 (0.2) | 0.4 (0.2) | 0.2 (0.05) | 0.4 (0.1) | 0.2 (0.03) | 0.3 (0.1) | 0.3 (0.03) | 0.3 (0.05) | 0.2 (0.1) |
wavelet.HLH_glcm_DifferenceAverage | wavelet.HLH_glcm_DifferenceAverage | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
wavelet.HHH_firstorder_RootMeanSquared | wavelet.HHH_firstorder_RootMeanSquared | Min / Max | 0.7 / 0.9 | 0 / 1.0 | 0.2 / 1.0 | 0.8 / 0.9 | 0.7 / 0.9 | 0.7 / 0.8 | 0.7 / 0.9 | 0.8 / 0.9 | 0.8 / 0.9 | 0.6 / 1.0 |
wavelet.HHH_firstorder_RootMeanSquared | wavelet.HHH_firstorder_RootMeanSquared | Med [IQR] | 0.8 [0.8;0.8] | 0.7 [0.6;0.8] | 0.8 [0.7;0.8] | 0.8 [0.8;0.8] | 0.8 [0.8;0.8] | 0.8 [0.8;0.8] | 0.8 [0.8;0.8] | 0.8 [0.8;0.8] | 0.8 [0.8;0.8] | 0.8 [0.7;0.8] |
wavelet.HHH_firstorder_RootMeanSquared | wavelet.HHH_firstorder_RootMeanSquared | Mean (std) | 0.8 (0.04) | 0.6 (0.2) | 0.7 (0.1) | 0.8 (0.02) | 0.8 (0.04) | 0.8 (0.03) | 0.8 (0.03) | 0.8 (0.02) | 0.8 (0.03) | 0.8 (0.1) |
wavelet.HHH_firstorder_RootMeanSquared | wavelet.HHH_firstorder_RootMeanSquared | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
wavelet.LHL_gldm_DependenceVariance | wavelet.LHL_gldm_DependenceVariance | Min / Max | 0.1 / 1.0 | 0.008 / 0.6 | 0 / 0.7 | 0.1 / 0.7 | 0.1 / 0.8 | 0.1 / 0.9 | 0.1 / 0.7 | 0.1 / 0.6 | 0.1 / 0.8 | 0.1 / 0.7 |
wavelet.LHL_gldm_DependenceVariance | wavelet.LHL_gldm_DependenceVariance | Med [IQR] | 0.4 [0.3;0.6] | 0.1 [0.1;0.2] | 0.2 [0.1;0.2] | 0.3 [0.2;0.4] | 0.3 [0.2;0.5] | 0.6 [0.4;0.7] | 0.3 [0.2;0.5] | 0.3 [0.2;0.5] | 0.4 [0.3;0.5] | 0.3 [0.2;0.4] |
wavelet.LHL_gldm_DependenceVariance | wavelet.LHL_gldm_DependenceVariance | Mean (std) | 0.5 (0.2) | 0.2 (0.1) | 0.2 (0.2) | 0.3 (0.2) | 0.4 (0.2) | 0.5 (0.2) | 0.4 (0.2) | 0.3 (0.2) | 0.4 (0.2) | 0.3 (0.2) |
wavelet.LHL_gldm_DependenceVariance | wavelet.LHL_gldm_DependenceVariance | N (NA) | 44 (0) | 34 (0) | 37 (0) | 32 (0) | 28 (0) | 50 (0) | 44 (0) | 29 (0) | 37 (0) | 42 (0) |
Histology | Histology | adenocarcinoma | 7 (13.73%) | 7 (13.73%) | 5 (9.80%) | 7 (13.73%) | 5 (9.80%) | 4 (7.84%) | 3 (5.88%) | 4 (7.84%) | 4 (7.84%) | 5 (9.80%) |
Histology | Histology | large cell | 13 (11.40%) | 6 (5.26%) | 16 (14.04%) | 11 (9.65%) | 7 (6.14%) | 14 (12.28%) | 16 (14.04%) | 9 (7.89%) | 12 (10.53%) | 10 (8.77%) |
Histology | Histology | nos | 3 (4.92%) | 7 (11.48%) | 10 (16.39%) | 5 (8.20%) | 2 (3.28%) | 7 (11.48%) | 4 (6.56%) | 8 (13.11%) | 8 (13.11%) | 7 (11.48%) |
Histology | Histology | squamous cell carcinoma | 21 (13.91%) | 14 (9.27%) | 6 (3.97%) | 9 (5.96%) | 14 (9.27%) | 25 (16.56%) | 21 (13.91%) | 8 (5.30%) | 13 (8.61%) | 20 (13.25%) |
Manufacturer | Manufacturer | CMS Inc. | 1 (1.19%) | 3 (3.57%) | 10 (11.90%) | 5 (5.95%) | 13 (15.48%) | 5 (5.95%) | 13 (15.48%) | 14 (16.67%) | 15 (17.86%) | 5 (5.95%) |
Manufacturer | Manufacturer | SIEMENS | 43 (14.68%) | 31 (10.58%) | 27 (9.22%) | 27 (9.22%) | 15 (5.12%) | 45 (15.36%) | 31 (10.58%) | 15 (5.12%) | 22 (7.51%) | 37 (12.63%) |
Overall.Stage | Overall.Stage | I | 5 (7.69%) | 5 (7.69%) | 11 (16.92%) | 3 (4.62%) | 5 (7.69%) | 2 (3.08%) | 3 (4.62%) | 7 (10.77%) | 9 (13.85%) | 15 (23.08%) |
Overall.Stage | Overall.Stage | II | 4 (10.53%) | 5 (13.16%) | 1 (2.63%) | 2 (5.26%) | 5 (13.16%) | 6 (15.79%) | 4 (10.53%) | 3 (7.89%) | 7 (18.42%) | 1 (2.63%) |
Overall.Stage | Overall.Stage | IIIa | 20 (18.52%) | 12 (11.11%) | 10 (9.26%) | 9 (8.33%) | 7 (6.48%) | 7 (6.48%) | 15 (13.89%) | 4 (3.70%) | 11 (10.19%) | 13 (12.04%) |
Overall.Stage | Overall.Stage | IIIb | 15 (9.04%) | 12 (7.23%) | 15 (9.04%) | 18 (10.84%) | 11 (6.63%) | 35 (21.08%) | 22 (13.25%) | 15 (9.04%) | 10 (6.02%) | 13 (7.83%) |
clinical.T.Stage | clinical.T.Stage | 1 | 3 (4.41%) | 21 (30.88%) | 15 (22.06%) | 2 (2.94%) | 1 (1.47%) | 0 (0%) | 1 (1.47%) | 0 (0%) | 6 (8.82%) | 19 (27.94%) |
clinical.T.Stage | clinical.T.Stage | 2 | 28 (18.79%) | 5 (3.36%) | 12 (8.05%) | 12 (8.05%) | 10 (6.71%) | 12 (8.05%) | 20 (13.42%) | 15 (10.07%) | 19 (12.75%) | 16 (10.74%) |
clinical.T.Stage | clinical.T.Stage | 3 | 4 (8.00%) | 2 (4.00%) | 3 (6.00%) | 8 (16.00%) | 9 (18.00%) | 8 (16.00%) | 4 (8.00%) | 4 (8.00%) | 5 (10.00%) | 3 (6.00%) |
clinical.T.Stage | clinical.T.Stage | 4 | 9 (8.18%) | 6 (5.45%) | 7 (6.36%) | 10 (9.09%) | 8 (7.27%) | 30 (27.27%) | 19 (17.27%) | 10 (9.09%) | 7 (6.36%) | 4 (3.64%) |
Clinical.N.Stage | Clinical.N.Stage | 0 | 11 (7.91%) | 10 (7.19%) | 15 (10.79%) | 11 (7.91%) | 11 (7.91%) | 20 (14.39%) | 14 (10.07%) | 15 (10.79%) | 17 (12.23%) | 15 (10.79%) |
Clinical.N.Stage | Clinical.N.Stage | 1_2 | 21 (13.46%) | 13 (8.33%) | 13 (8.33%) | 13 (8.33%) | 12 (7.69%) | 20 (12.82%) | 23 (14.74%) | 8 (5.13%) | 16 (10.26%) | 17 (10.90%) |
Clinical.N.Stage | Clinical.N.Stage | 3 | 12 (14.63%) | 11 (13.41%) | 9 (10.98%) | 8 (9.76%) | 5 (6.10%) | 10 (12.20%) | 7 (8.54%) | 6 (7.32%) | 4 (4.88%) | 10 (12.20%) |
Then I want to evaluate possible relationship between clusters generated and histology class.
# I generate a cross table to check distribution of histology classes with partitions
table_hist_clust_res_varsel <- table(lung_data_res_varsel$Histology,lung_data_res_varsel$partition)
addmargins(prop.table(table_hist_clust_res_varsel)) %>% round(3) %>% kable(full_with = FALSE) %>% kable_classic_2()
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | Sum | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| adenocarcinoma | 0.019 | 0.019 | 0.013 | 0.019 | 0.013 | 0.011 | 0.008 | 0.011 | 0.011 | 0.013 | 0.135 |
| large cell | 0.034 | 0.016 | 0.042 | 0.029 | 0.019 | 0.037 | 0.042 | 0.024 | 0.032 | 0.027 | 0.302 |
| nos | 0.008 | 0.019 | 0.027 | 0.013 | 0.005 | 0.019 | 0.011 | 0.021 | 0.021 | 0.019 | 0.162 |
| squamous cell carcinoma | 0.056 | 0.037 | 0.016 | 0.024 | 0.037 | 0.066 | 0.056 | 0.021 | 0.034 | 0.053 | 0.401 |
| Sum | 0.117 | 0.090 | 0.098 | 0.085 | 0.074 | 0.133 | 0.117 | 0.077 | 0.098 | 0.111 | 1.000 |
# I display distribution of histology class within different clusters
ggplot(lung_data_res_varsel,aes(partition, fill = Histology)) +
geom_bar(position = 'fill',alpha = .5) +
coord_flip()+
theme_bw()
ggsave("figures/Fig31.tiff",width = 100, height = 50, device="tiff", dpi=300, units = "mm", scale = 1)
# I continue with a correspondence analysis and generate an assymetric representation using
# columns (clusters) repsented with the principal coordinates and histology classes
# represented with standard coordinates
ca.res_varsel <- ca(table_hist_clust_res_varsel)
tiff(filename = "figures/Fig32.tiff", width = 200, height = 200, units = "mm", res = 300)
plot(ca.res_varsel, map= "colprincipal")
fig <- dev.off()
plot(ca.res_varsel, map= "colprincipal")
# From CA analisis I get number of clusters with the greatest inertias to further compare cluster characteristics
clust_top_inertias <- data.frame(clust = ca.res_varsel$colnames, inertia = get_ca_col(ca.res_varsel)$inertia) %>% arrange(desc(inertia)) %>% top_n(2)
# After evaluating main clusters associated with different histology classes we can focus our analysis in
# describing differences between these clusters:
lung_data_res_varsel_selected <- lung_data_res_varsel %>% filter(partition %in% clust_top_inertias$clust) %>% droplevels()
lung_data_res_varsel_selected_table <- table(lung_data_res_varsel_selected$Histology,lung_data_res_varsel_selected$partition)
# I evaluate agreement using adjusted rand index (ARI) coefficient
cat('ARI (Histology class, Partitions):',ARI(lung_data_res_varsel_selected$Histology,lung_data_res_varsel_selected$partition))
## ARI (Histology class, Partitions): 0.05985412
# And I generate a cross table focusing on these clusters
crosstab5<- crosstable(lung_data_res_varsel_selected %>% select(.,-c(deadstatus.event,Survival.time)),
by = partition, test = TRUE)
(table25 <- crosstab5 %>% separate(test,
into = c('pvalue',
'test'),
sep = "\n") %>%
separate(pvalue,
into = c('string',
'pval'),
sep = ":",
convert = TRUE) %>%
arrange(pval) %>%
filter(variable != 'Med [IQR]',
variable != 'N (NA)',
variable != 'Min / Max') %>%
dplyr::select(.,-c(string,.id)) %>% kable(full_with = FALSE) %>% kable_classic_2())
| label | variable | 3 | 7 | pval | test |
|---|---|---|---|---|---|
| original_shape_VoxelVolume | Mean (std) | 0.4 (0.1) | 0.7 (0.1) | <0.0001 | (Two Sample t-test) |
| log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis | Mean (std) | 0.05 (0.05) | 0.2 (0.1) | <0.0001 | (Wilcoxon rank sum exact test) |
| log.sigma.2.0.mm.3D_glcm_Autocorrelation | Mean (std) | 0.2 (0.1) | 0.3 (0.1) | <0.0001 | (Wilcoxon rank sum exact test) |
| wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis | Mean (std) | 0.3 (0.1) | 0.7 (0.1) | <0.0001 | (Welch Two Sample t-test) |
| wavelet.HHH_glszm_GrayLevelNonUniformity | Mean (std) | 0.02 (0.03) | 0.1 (0.1) | <0.0001 | (Wilcoxon rank sum test) |
| wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 0.2 (0.1) | 0.4 (0.1) | <0.0001 | (Welch Two Sample t-test) |
| wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis | Mean (std) | 0.1 (0.05) | 0.02 (0.007) | <0.0001 | (Wilcoxon rank sum exact test) |
| wavelet.HHH_glcm_ClusterProminence | Mean (std) | 0.1 (0.05) | 0.1 (0.02) | <0.0001 | (Wilcoxon rank sum exact test) |
| wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 0.2 (0.1) | 0.3 (0.1) | <0.0001 | (Two Sample t-test) |
| wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis | Mean (std) | 0.4 (0.1) | 0.7 (0.1) | <0.0001 | (Welch Two Sample t-test) |
| wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 0.1 (0.04) | 0.03 (0.01) | <0.0001 | (Wilcoxon rank sum exact test) |
| wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis | Mean (std) | 0.2 (0.1) | 0.1 (0.03) | <0.0001 | (Wilcoxon rank sum exact test) |
| log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 0.2 (0.1) | 0.4 (0.1) | <0.0001 | (Wilcoxon rank sum exact test) |
| wavelet.LHH_glszm_GrayLevelNonUniformity | Mean (std) | 0.1 (0.1) | 0.4 (0.2) | <0.0001 | (Wilcoxon rank sum exact test) |
| log.sigma.5.0.mm.3D_firstorder_Kurtosis | Mean (std) | 0.05 (0.03) | 0.1 (0.1) | <0.0001 | (Wilcoxon rank sum exact test) |
| log.sigma.5.0.mm.3D_firstorder_90Percentile | Mean (std) | 0.8 (0.1) | 0.9 (0.03) | <0.0001 | (Welch Two Sample t-test) |
| wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 0.2 (0.1) | 0.1 (0.02) | <0.0001 | (Wilcoxon rank sum exact test) |
| wavelet.LHL_gldm_DependenceVariance | Mean (std) | 0.2 (0.2) | 0.4 (0.2) | 0.0001 | (Wilcoxon rank sum exact test) |
| clinical.T.Stage | 1 | 15 (93.75%) | 1 (6.25%) | 0.0001 | (Fisher’s Exact Test for Count Data) |
| clinical.T.Stage | 2 | 12 (37.50%) | 20 (62.50%) | 0.0001 | (Fisher’s Exact Test for Count Data) |
| clinical.T.Stage | 3 | 3 (42.86%) | 4 (57.14%) | 0.0001 | (Fisher’s Exact Test for Count Data) |
| clinical.T.Stage | 4 | 7 (26.92%) | 19 (73.08%) | 0.0001 | (Fisher’s Exact Test for Count Data) |
| wavelet.HHH_firstorder_RootMeanSquared | Mean (std) | 0.7 (0.1) | 0.8 (0.03) | 0.0002 | (Wilcoxon rank sum exact test) |
| wavelet.HHH_glcm_Imc1 | Mean (std) | 0.8 (0.1) | 0.7 (0.1) | 0.0018 | (Wilcoxon rank sum exact test) |
| Histology | adenocarcinoma | 5 (62.50%) | 3 (37.50%) | 0.0109 | (Fisher’s Exact Test for Count Data) |
| Histology | large cell | 16 (50.00%) | 16 (50.00%) | 0.0109 | (Fisher’s Exact Test for Count Data) |
| Histology | nos | 10 (71.43%) | 4 (28.57%) | 0.0109 | (Fisher’s Exact Test for Count Data) |
| Histology | squamous cell carcinoma | 6 (22.22%) | 21 (77.78%) | 0.0109 | (Fisher’s Exact Test for Count Data) |
| wavelet.LHH_glcm_Imc1 | Mean (std) | 0.6 (0.2) | 0.7 (0.1) | 0.0123 | (Wilcoxon rank sum exact test) |
| original_glcm_Imc1 | Mean (std) | 0.6 (0.1) | 0.6 (0.1) | 0.0336 | (Wilcoxon rank sum exact test) |
| Overall.Stage | I | 11 (78.57%) | 3 (21.43%) | 0.0402 | (Fisher’s Exact Test for Count Data) |
| Overall.Stage | II | 1 (20.00%) | 4 (80.00%) | 0.0402 | (Fisher’s Exact Test for Count Data) |
| Overall.Stage | IIIa | 10 (40.00%) | 15 (60.00%) | 0.0402 | (Fisher’s Exact Test for Count Data) |
| Overall.Stage | IIIb | 15 (40.54%) | 22 (59.46%) | 0.0402 | (Fisher’s Exact Test for Count Data) |
| wavelet.HHH_glszm_GrayLevelNonUniformityNormalized | Mean (std) | 0.2 (0.2) | 0.3 (0.1) | 0.0501 | (Wilcoxon rank sum test) |
| wavelet.HLH_glcm_DifferenceAverage | Mean (std) | 0.4 (0.2) | 0.3 (0.1) | 0.0622 | (Wilcoxon rank sum exact test) |
| wavelet.LLL_glcm_ClusterProminence | Mean (std) | 0.7 (0.1) | 0.7 (0.1) | 0.0814 | (Two Sample t-test) |
| wavelet.HHL_glcm_ClusterProminence | Mean (std) | 0.2 (0.1) | 0.2 (0.1) | 0.0925 | (Wilcoxon rank sum exact test) |
| wavelet.LHH_firstorder_Skewness | Mean (std) | 0.5 (0.1) | 0.5 (0.05) | 0.2208 | (Wilcoxon rank sum exact test) |
| Clinical.N.Stage | 0 | 15 (51.72%) | 14 (48.28%) | 0.2900 | (Pearson’s Chi-squared test) |
| Clinical.N.Stage | 1_2 | 13 (36.11%) | 23 (63.89%) | 0.2900 | (Pearson’s Chi-squared test) |
| Clinical.N.Stage | 3 | 9 (56.25%) | 7 (43.75%) | 0.2900 | (Pearson’s Chi-squared test) |
| gender | female | 16 (51.61%) | 15 (48.39%) | 0.3986 | (Pearson’s Chi-squared test) |
| gender | male | 21 (42.00%) | 29 (58.00%) | 0.3986 | (Pearson’s Chi-squared test) |
| log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized | Mean (std) | 0.5 (0.2) | 0.5 (0.1) | 0.5070 | (Two Sample t-test) |
| age | Mean (std) | 0.6 (0.2) | 0.6 (0.2) | 0.6372 | (Two Sample t-test) |
| wavelet.HHH_glrlm_GrayLevelVariance | Mean (std) | 0.1 (0.1) | 0.1 (0.03) | 0.7307 | (Wilcoxon rank sum exact test) |
| wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis | Mean (std) | 0.1 (0.05) | 0.1 (0.03) | 0.7665 | (Wilcoxon rank sum exact test) |
| log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 0.04 (0.03) | 0.04 (0.03) | 0.7954 | (Wilcoxon rank sum exact test) |
| Manufacturer | CMS Inc. | 10 (43.48%) | 13 (56.52%) | 0.8023 | (Pearson’s Chi-squared test) |
| Manufacturer | SIEMENS | 27 (46.55%) | 31 (53.45%) | 0.8023 | (Pearson’s Chi-squared test) |
table25 %>% save_kable("tables/table25.png")
# I generate a cross table to check distribution of histology classes with partitions
table_stage_clust_res_varsel <- table(lung_data_res_varsel$Overall.Stage,lung_data_res_varsel$partition)
addmargins(prop.table(table_stage_clust_res_varsel)) %>% round(3) %>% kable(full_with = FALSE) %>% kable_classic_2()
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | Sum | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| I | 0.013 | 0.013 | 0.029 | 0.008 | 0.013 | 0.005 | 0.008 | 0.019 | 0.024 | 0.040 | 0.172 |
| II | 0.011 | 0.013 | 0.003 | 0.005 | 0.013 | 0.016 | 0.011 | 0.008 | 0.019 | 0.003 | 0.101 |
| IIIa | 0.053 | 0.032 | 0.027 | 0.024 | 0.019 | 0.019 | 0.040 | 0.011 | 0.029 | 0.034 | 0.286 |
| IIIb | 0.040 | 0.032 | 0.040 | 0.048 | 0.029 | 0.093 | 0.058 | 0.040 | 0.027 | 0.034 | 0.440 |
| Sum | 0.117 | 0.090 | 0.098 | 0.085 | 0.074 | 0.133 | 0.117 | 0.077 | 0.098 | 0.111 | 1.000 |
# I display distribution of histology class within different clusters
ggplot(lung_data_res_varsel,aes(partition, fill = Overall.Stage)) +
geom_bar(position = 'fill',alpha = .5) +
coord_flip()+
theme_bw()
ggsave("figures/Fig31.1.tiff",width = 100, height = 50, device="tiff", dpi=300, units = "mm", scale = 1)
# I continue with a correspondence analysis and generate an assymetric representation using
# columns (clusters) repsented with the principal coordinates and histology classes
# represented with standard coordinates
ca.res_varsel_stage <- ca(table_stage_clust_res_varsel)
tiff(filename = "figures/Fig32.1.tiff", width = 200, height = 200, units = "mm", res = 300)
plot(ca.res_varsel_stage, map= "colprincipal")
fig <- dev.off()
plot(ca.res_varsel_stage, map= "colprincipal")
# From CA analisis I get number of clusters with the greatest inertias to further compare cluster characteristics
clust_top_inertias_stage <- data.frame(clust = ca.res_varsel_stage$colnames, inertia = get_ca_col(ca.res_varsel_stage)$inertia) %>% arrange(desc(inertia)) %>% top_n(2)
# After evaluating main clusters associated with different histology classes we can focus our analysis in
# describing differences between these clusters:
lung_data_res_varsel_selected_stage <- lung_data_res_varsel %>% filter(partition %in% clust_top_inertias_stage$clust) %>% droplevels()
lung_data_res_varsel_selected_table_stage <- table(lung_data_res_varsel_selected$Overall.Stage,lung_data_res_varsel_selected$partition)
# I evaluate agreement using adjusted rand index (ARI) coefficient
cat('ARI (Histology class, Partitions):',ARI(lung_data_res_varsel_selected_stage$Overall.Stage,lung_data_res_varsel_selected_stage$partition))
## ARI (Histology class, Partitions): 0.1510967
# And I generate a cross table focusing on these clusters
crosstab5.1<- crosstable(lung_data_res_varsel_selected_stage %>% select(.,-c(deadstatus.event,Survival.time)),
by = partition, test = TRUE)
(table25.1 <- crosstab5.1 %>% separate(test,
into = c('pvalue',
'test'),
sep = "\n") %>%
separate(pvalue,
into = c('string',
'pval'),
sep = ":",
convert = TRUE) %>%
arrange(pval) %>%
filter(variable != 'Med [IQR]',
variable != 'N (NA)',
variable != 'Min / Max') %>%
dplyr::select(.,-c(string,.id)) %>% kable(full_with = FALSE) %>% kable_classic_2())
| label | variable | 6 | 10 | pval | test |
|---|---|---|---|---|---|
| original_shape_VoxelVolume | Mean (std) | 0.8 (0.1) | 0.4 (0.1) | <0.0001 | (Welch Two Sample t-test) |
| wavelet.HHL_glcm_ClusterProminence | Mean (std) | 0.02 (0.02) | 0.1 (0.04) | <0.0001 | (Wilcoxon rank sum test) |
| log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis | Mean (std) | 0.4 (0.2) | 0.1 (0.05) | <0.0001 | (Wilcoxon rank sum test) |
| log.sigma.2.0.mm.3D_glcm_Autocorrelation | Mean (std) | 0.2 (0.1) | 0.1 (0.1) | <0.0001 | (Wilcoxon rank sum test) |
| wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis | Mean (std) | 0.8 (0.1) | 0.4 (0.1) | <0.0001 | (Two Sample t-test) |
| wavelet.HHH_glszm_GrayLevelNonUniformity | Mean (std) | 0.04 (0.03) | 0.01 (0.01) | <0.0001 | (Wilcoxon rank sum test) |
| wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 0.4 (0.1) | 0.2 (0.1) | <0.0001 | (Welch Two Sample t-test) |
| wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis | Mean (std) | 0.02 (0.009) | 0.1 (0.04) | <0.0001 | (Welch Two Sample t-test) |
| wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 0.4 (0.1) | 0.1 (0.1) | <0.0001 | (Two Sample t-test) |
| wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis | Mean (std) | 0.8 (0.1) | 0.4 (0.1) | <0.0001 | (Two Sample t-test) |
| wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 0.1 (0.03) | 0.2 (0.1) | <0.0001 | (Wilcoxon rank sum test) |
| wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis | Mean (std) | 0.1 (0.03) | 0.3 (0.1) | <0.0001 | (Welch Two Sample t-test) |
| wavelet.HHH_glrlm_GrayLevelVariance | Mean (std) | 0.03 (0.002) | 0.02 (4e-04) | <0.0001 | (Wilcoxon rank sum test) |
| log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 0.5 (0.1) | 0.1 (0.1) | <0.0001 | (Welch Two Sample t-test) |
| wavelet.LHH_glszm_GrayLevelNonUniformity | Mean (std) | 0.2 (0.1) | 0.1 (0.03) | <0.0001 | (Wilcoxon rank sum test) |
| log.sigma.5.0.mm.3D_firstorder_Kurtosis | Mean (std) | 0.1 (0.1) | 0.05 (0.1) | <0.0001 | (Wilcoxon rank sum test) |
| wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 0.2 (0.1) | 0.3 (0.2) | <0.0001 | (Wilcoxon rank sum test) |
| wavelet.LHL_gldm_DependenceVariance | Mean (std) | 0.5 (0.2) | 0.3 (0.2) | <0.0001 | (Wilcoxon rank sum test) |
| Overall.Stage | I | 2 (11.76%) | 15 (88.24%) | <0.0001 | (Fisher’s Exact Test for Count Data) |
| Overall.Stage | II | 6 (85.71%) | 1 (14.29%) | <0.0001 | (Fisher’s Exact Test for Count Data) |
| Overall.Stage | IIIa | 7 (35.00%) | 13 (65.00%) | <0.0001 | (Fisher’s Exact Test for Count Data) |
| Overall.Stage | IIIb | 35 (72.92%) | 13 (27.08%) | <0.0001 | (Fisher’s Exact Test for Count Data) |
| clinical.T.Stage | 1 | 0 (0%) | 19 (100.00%) | <0.0001 | (Pearson’s Chi-squared test) |
| clinical.T.Stage | 2 | 12 (42.86%) | 16 (57.14%) | <0.0001 | (Pearson’s Chi-squared test) |
| clinical.T.Stage | 3 | 8 (72.73%) | 3 (27.27%) | <0.0001 | (Pearson’s Chi-squared test) |
| clinical.T.Stage | 4 | 30 (88.24%) | 4 (11.76%) | <0.0001 | (Pearson’s Chi-squared test) |
| wavelet.LHH_glcm_Imc1 | Mean (std) | 0.7 (0.1) | 0.6 (0.1) | 0.0009 | (Wilcoxon rank sum test) |
| log.sigma.5.0.mm.3D_firstorder_90Percentile | Mean (std) | 0.9 (0.02) | 0.8 (0.1) | 0.0018 | (Wilcoxon rank sum test) |
| wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis | Mean (std) | 0.01 (0.007) | 0.02 (0.01) | 0.0113 | (Wilcoxon rank sum test) |
| wavelet.HHH_glcm_ClusterProminence | Mean (std) | 0.1 (0.02) | 0.1 (0.04) | 0.0183 | (Welch Two Sample t-test) |
| original_glcm_Imc1 | Mean (std) | 0.6 (0.1) | 0.6 (0.1) | 0.0203 | (Wilcoxon rank sum test) |
| gender | female | 12 (40.00%) | 18 (60.00%) | 0.0546 | (Pearson’s Chi-squared test) |
| gender | male | 38 (61.29%) | 24 (38.71%) | 0.0546 | (Pearson’s Chi-squared test) |
| wavelet.LLL_glcm_ClusterProminence | Mean (std) | 0.5 (0.2) | 0.6 (0.1) | 0.0548 | (Wilcoxon rank sum test) |
| wavelet.LHH_firstorder_Skewness | Mean (std) | 0.5 (0.1) | 0.5 (0.1) | 0.1264 | (Wilcoxon rank sum test) |
| wavelet.HHH_glcm_Imc1 | Mean (std) | 0.8 (0.1) | 0.8 (0.1) | 0.1283 | (Wilcoxon rank sum test) |
| wavelet.HHH_glszm_GrayLevelNonUniformityNormalized | Mean (std) | 0.6 (0.2) | 0.6 (0.1) | 0.4420 | (Wilcoxon rank sum test) |
| log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 0.03 (0.02) | 0.04 (0.1) | 0.5255 | (Wilcoxon rank sum test) |
| log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized | Mean (std) | 0.5 (0.1) | 0.4 (0.2) | 0.5832 | (Wilcoxon rank sum test) |
| age | Mean (std) | 0.5 (0.2) | 0.6 (0.2) | 0.5985 | (Two Sample t-test) |
| wavelet.HHH_firstorder_RootMeanSquared | Mean (std) | 0.8 (0.03) | 0.8 (0.1) | 0.7718 | (Wilcoxon rank sum test) |
| Clinical.N.Stage | 0 | 20 (57.14%) | 15 (42.86%) | 0.8764 | (Pearson’s Chi-squared test) |
| Clinical.N.Stage | 1_2 | 20 (54.05%) | 17 (45.95%) | 0.8764 | (Pearson’s Chi-squared test) |
| Clinical.N.Stage | 3 | 10 (50.00%) | 10 (50.00%) | 0.8764 | (Pearson’s Chi-squared test) |
| wavelet.HLH_glcm_DifferenceAverage | Mean (std) | 0.2 (0.03) | 0.2 (0.1) | 0.8907 | (Welch Two Sample t-test) |
| Histology | adenocarcinoma | 4 (44.44%) | 5 (55.56%) | 0.9043 | (Fisher’s Exact Test for Count Data) |
| Histology | large cell | 14 (58.33%) | 10 (41.67%) | 0.9043 | (Fisher’s Exact Test for Count Data) |
| Histology | nos | 7 (50.00%) | 7 (50.00%) | 0.9043 | (Fisher’s Exact Test for Count Data) |
| Histology | squamous cell carcinoma | 25 (55.56%) | 20 (44.44%) | 0.9043 | (Fisher’s Exact Test for Count Data) |
| Manufacturer | CMS Inc. | 5 (50.00%) | 5 (50.00%) | 1.0000 | (Fisher’s Exact Test for Count Data) |
| Manufacturer | SIEMENS | 45 (54.88%) | 37 (45.12%) | 1.0000 | (Fisher’s Exact Test for Count Data) |
table25.1 %>% save_kable("tables/table25.1.png")
Finally I want to analyze survival probabilities among different clusters.
# I generate a specific dataframe including survival, dead status event and partition variables
df_surv_res_varsel <- data.frame(Survival.time = lung_data_no_out$Survival.time,
deadstatus.event = lung_data_no_out$deadstatus.event,
partition = res_varsel@partitions@zMAP)
# I create a survival object, and use it as response variable
# to create Kaplan-Meier survival curves for each cluster
sfit_res_varsel <- survfit(Surv(Survival.time, as.numeric(deadstatus.event)) ~ partition,
data = df_surv_res_varsel)
# I plot survival curve including confidence intervals for each curve and
# p value result from log-rank test to evaluate difference in survival between groups
tiff(filename = "figures/Fig33.tiff", width = 300, height = 200, units = "mm", res = 300)
(surv <- ggsurvplot(sfit_res_varsel,
surv.median.line = 'hv',
pval=TRUE,
risk.table='percentage',
surv.plot.height = 0.7,
tables.height = 0.3))
fig <- dev.off()
(surv <- ggsurvplot(sfit_res_varsel,
surv.median.line = 'hv',
pval=TRUE,
risk.table='percentage',
surv.plot.height = 0.7,
tables.height = 0.3))
min_max_surv <- surv_median(sfit_res_varsel) %>%
filter(median == min(median) | median == max(median)) %>%
select(strata) %>%
separate(strata,
into = c('string',
'partition_num'),
sep = "=",
convert = TRUE)
min_max_surv$partition_num
## [1] 3 8
# I repeat log-rank test comparing only the two most extreme median survival groups
surv_pvalue(
survfit(Surv(Survival.time, as.numeric(deadstatus.event)) ~ partition,
data = df_surv_res_varsel %>% filter(partition %in% min_max_surv$partition_num)),
method = "survdiff",
test.for.trend = FALSE,
combine = FALSE
)
## variable pval method pval.txt
## 1 partition 0.1244872 Log-rank p = 0.12
# And describe composition for the clusters with most extreme results on survival analisis
crosstab6<- crosstable(lung_data_res_varsel %>% filter(partition %in% min_max_surv$partition_num) %>% select(.,-c(deadstatus.event,Survival.time)) %>% droplevels(),
by = partition, test = TRUE)
(table26 <- crosstab6 %>% separate(test,
into = c('pvalue',
'test'),
sep = "\n") %>%
separate(pvalue,
into = c('string',
'pval'),
sep = ":",
convert = TRUE) %>%
arrange(pval) %>%
filter(variable != 'Med [IQR]',
variable != 'N (NA)',
variable != 'Min / Max') %>%
dplyr::select(.,-c(string,.id)) %>% kable(full_with = FALSE) %>% kable_classic_2())
| label | variable | 3 | 8 | pval | test |
|---|---|---|---|---|---|
| original_shape_VoxelVolume | Mean (std) | 0.4 (0.1) | 0.9 (0.1) | <0.0001 | (Welch Two Sample t-test) |
| wavelet.HHL_glcm_ClusterProminence | Mean (std) | 0.2 (0.1) | 0.1 (0.04) | <0.0001 | (Wilcoxon rank sum exact test) |
| log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis | Mean (std) | 0.05 (0.05) | 0.5 (0.2) | <0.0001 | (Wilcoxon rank sum exact test) |
| wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis | Mean (std) | 0.3 (0.1) | 0.8 (0.1) | <0.0001 | (Two Sample t-test) |
| wavelet.HHH_glszm_GrayLevelNonUniformity | Mean (std) | 0.02 (0.03) | 0.1 (0.1) | <0.0001 | (Wilcoxon rank sum test) |
| wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 0.2 (0.1) | 0.4 (0.1) | <0.0001 | (Welch Two Sample t-test) |
| wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis | Mean (std) | 0.1 (0.05) | 0.01 (0.009) | <0.0001 | (Wilcoxon rank sum exact test) |
| wavelet.HHH_glcm_ClusterProminence | Mean (std) | 0.1 (0.05) | 0.02 (0.01) | <0.0001 | (Wilcoxon rank sum exact test) |
| wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 0.2 (0.1) | 0.4 (0.1) | <0.0001 | (Welch Two Sample t-test) |
| wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis | Mean (std) | 0.4 (0.1) | 0.8 (0.1) | <0.0001 | (Welch Two Sample t-test) |
| wavelet.LLL_glcm_ClusterProminence | Mean (std) | 0.7 (0.1) | 0.5 (0.1) | <0.0001 | (Wilcoxon rank sum exact test) |
| wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 0.1 (0.04) | 0.03 (0.02) | <0.0001 | (Wilcoxon rank sum exact test) |
| wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis | Mean (std) | 0.2 (0.1) | 0.1 (0.04) | <0.0001 | (Wilcoxon rank sum exact test) |
| wavelet.HHH_glcm_Imc1 | Mean (std) | 0.8 (0.1) | 0.5 (0.1) | <0.0001 | (Wilcoxon rank sum exact test) |
| log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis | Mean (std) | 0.2 (0.1) | 0.5 (0.1) | <0.0001 | (Wilcoxon rank sum exact test) |
| wavelet.LHH_glszm_GrayLevelNonUniformity | Mean (std) | 0.1 (0.1) | 0.4 (0.2) | <0.0001 | (Welch Two Sample t-test) |
| wavelet.LHH_glcm_Imc1 | Mean (std) | 0.6 (0.2) | 0.8 (0.1) | <0.0001 | (Wilcoxon rank sum exact test) |
| log.sigma.5.0.mm.3D_firstorder_Kurtosis | Mean (std) | 0.05 (0.03) | 0.2 (0.2) | <0.0001 | (Wilcoxon rank sum exact test) |
| wavelet.HHH_glrlm_GrayLevelVariance | Mean (std) | 0.1 (0.1) | 0.04 (0.01) | 0.0001 | (Wilcoxon rank sum exact test) |
| log.sigma.5.0.mm.3D_firstorder_90Percentile | Mean (std) | 0.8 (0.1) | 0.9 (0.02) | 0.0002 | (Wilcoxon rank sum exact test) |
| clinical.T.Stage | 1 | 15 (100.00%) | 0 (0%) | 0.0003 | (Fisher’s Exact Test for Count Data) |
| clinical.T.Stage | 2 | 12 (44.44%) | 15 (55.56%) | 0.0003 | (Fisher’s Exact Test for Count Data) |
| clinical.T.Stage | 3 | 3 (42.86%) | 4 (57.14%) | 0.0003 | (Fisher’s Exact Test for Count Data) |
| clinical.T.Stage | 4 | 7 (41.18%) | 10 (58.82%) | 0.0003 | (Fisher’s Exact Test for Count Data) |
| wavelet.LHL_gldm_DependenceVariance | Mean (std) | 0.2 (0.2) | 0.3 (0.2) | 0.0004 | (Wilcoxon rank sum exact test) |
| wavelet.HLH_glcm_DifferenceAverage | Mean (std) | 0.4 (0.2) | 0.3 (0.03) | 0.0008 | (Wilcoxon rank sum exact test) |
| wavelet.HHH_firstorder_RootMeanSquared | Mean (std) | 0.7 (0.1) | 0.8 (0.02) | 0.0008 | (Wilcoxon rank sum exact test) |
| wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis | Mean (std) | 0.1 (0.05) | 0.1 (0.03) | 0.0042 | (Welch Two Sample t-test) |
| log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized | Mean (std) | 0.5 (0.2) | 0.4 (0.1) | 0.0045 | (Two Sample t-test) |
| wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 0.2 (0.1) | 0.1 (0.1) | 0.0095 | (Wilcoxon rank sum exact test) |
| gender | female | 16 (76.19%) | 5 (23.81%) | 0.0244 | (Pearson’s Chi-squared test) |
| gender | male | 21 (46.67%) | 24 (53.33%) | 0.0244 | (Pearson’s Chi-squared test) |
| age | Mean (std) | 0.6 (0.2) | 0.7 (0.1) | 0.0747 | (Two Sample t-test) |
| Manufacturer | CMS Inc. | 10 (41.67%) | 14 (58.33%) | 0.0749 | (Pearson’s Chi-squared test) |
| Manufacturer | SIEMENS | 27 (64.29%) | 15 (35.71%) | 0.0749 | (Pearson’s Chi-squared test) |
| wavelet.HHH_glszm_GrayLevelNonUniformityNormalized | Mean (std) | 0.2 (0.2) | 0.3 (0.2) | 0.3322 | (Wilcoxon rank sum test) |
| Overall.Stage | I | 11 (61.11%) | 7 (38.89%) | 0.3410 | (Fisher’s Exact Test for Count Data) |
| Overall.Stage | II | 1 (25.00%) | 3 (75.00%) | 0.3410 | (Fisher’s Exact Test for Count Data) |
| Overall.Stage | IIIa | 10 (71.43%) | 4 (28.57%) | 0.3410 | (Fisher’s Exact Test for Count Data) |
| Overall.Stage | IIIb | 15 (50.00%) | 15 (50.00%) | 0.3410 | (Fisher’s Exact Test for Count Data) |
| original_glcm_Imc1 | Mean (std) | 0.6 (0.1) | 0.6 (0.2) | 0.4332 | (Wilcoxon rank sum exact test) |
| log.sigma.2.0.mm.3D_glcm_Autocorrelation | Mean (std) | 0.2 (0.1) | 0.2 (0.1) | 0.4720 | (Wilcoxon rank sum exact test) |
| Histology | adenocarcinoma | 5 (55.56%) | 4 (44.44%) | 0.6434 | (Fisher’s Exact Test for Count Data) |
| Histology | large cell | 16 (64.00%) | 9 (36.00%) | 0.6434 | (Fisher’s Exact Test for Count Data) |
| Histology | nos | 10 (55.56%) | 8 (44.44%) | 0.6434 | (Fisher’s Exact Test for Count Data) |
| Histology | squamous cell carcinoma | 6 (42.86%) | 8 (57.14%) | 0.6434 | (Fisher’s Exact Test for Count Data) |
| Clinical.N.Stage | 0 | 15 (50.00%) | 15 (50.00%) | 0.6593 | (Pearson’s Chi-squared test) |
| Clinical.N.Stage | 1_2 | 13 (61.90%) | 8 (38.10%) | 0.6593 | (Pearson’s Chi-squared test) |
| Clinical.N.Stage | 3 | 9 (60.00%) | 6 (40.00%) | 0.6593 | (Pearson’s Chi-squared test) |
| wavelet.LHH_firstorder_Skewness | Mean (std) | 0.5 (0.1) | 0.5 (0.1) | 0.9591 | (Wilcoxon rank sum exact test) |
| log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis | Mean (std) | 0.04 (0.03) | 0.03 (0.01) | 0.9898 | (Wilcoxon rank sum exact test) |
table26 %>% save_kable("tables/table26.png")
cat('ARI (Histology class, Partitions):',ARI(res_varsel@partitions@zMAP,res_varsel_select@partitions@zMAP))
## ARI (Histology class, Partitions): 0.4952459
res_varsel_select
## An object of class "VSLCMresults"
## Slot "data":
## An object of class "VSLCMdata"
## Slot "n":
## [1] 377
##
## Slot "d":
## [1] 33
##
## Slot "withContinuous":
## [1] TRUE
##
## Slot "withInteger":
## [1] FALSE
##
## Slot "withCategorical":
## [1] TRUE
##
## Slot "dataContinuous":
## An object of class "VSLCMdataContinuous"
## Slot "n":
## [1] 377
##
## Slot "d":
## [1] 32
##
## Slot "data":
## age original_shape_VoxelVolume wavelet.HHL_glcm_ClusterProminence
## [1,] 0.7767505 0.82083575 0.017461636
## [2,] 0.8637663 0.94953191 0.073434502
## [3,] 0.5945563 0.59183619 0.198118344
## [4,] 0.6410838 0.72619619 0.185592189
## [5,] 0.8065750 0.72259880 0.307397295
## [6,] 0.6928975 0.71581074 0.316596223
## [7,] 0.8246190 0.43542048 0.010826091
## [8,] 0.6546276 0.61528045 0.193514542
## [9,] 0.3869275 0.74914043 0.112877887
## [10,] 0.6441035 0.46818005 0.184736028
## [11,] 0.5282095 0.71703463 0.156773984
## [12,] 0.6475489 0.89982569 0.199829481
## [13,] 0.5460001 0.45244232 0.146744718
## [14,] 0.5702541 0.39345034 0.512214363
## [15,] 0.6556186 0.26267930 0.250592005
## [16,] 0.7829795 0.77158913 0.061528209
## [17,] 0.6931337 0.87792688 0.160083688
## [18,] 0.8498468 0.81004969 0.135467693
## [19,] 0.7089887 0.83162872 0.069545798
## [20,] 0.7741566 0.76696285 0.151906211
## [21,] 0.7255039 0.51562083 0.067569872
## [22,] 0.5937066 0.75977018 0.199817865
## [23,] 0.6319765 0.85109184 0.034090019
## [24,] 0.6086188 0.63130946 0.045085506
## [25,] 0.4957428 0.71839857 0.158967716
## [26,] 0.6554773 0.62879033 0.186606320
## [27,] 0.7240423 0.73570565 0.036725713
## [28,] 0.6362717 0.82238939 0.432036178
## [29,] 0.3747988 0.75538672 0.013209746
## [30,] 0.8397484 0.77530669 0.019927211
## [31,] 0.3502604 0.62762400 0.191853320
## [32,] 0.7078563 0.35448337 0.278854914
## [33,] 0.3199654 0.77990939 0.118498965
## [34,] 0.4298993 0.90836097 0.012708559
## [35,] 0.7053555 0.65531079 0.072738319
## [36,] 0.6615632 0.83623676 0.090962663
## [37,] 0.6958224 0.87811712 0.124342869
## [38,] 0.6724182 0.51182856 0.114400348
## [39,] 0.8021386 0.78524691 0.025544597
## [40,] 0.2209175 0.64866673 0.120449441
## [41,] 0.5369394 0.64879324 0.231814121
## [42,] 0.8107271 0.20154219 0.365073964
## [43,] 0.3428060 0.32605485 0.401276625
## [44,] 0.3103393 0.73610633 0.100644929
## [45,] 0.4920147 0.77989008 0.087389042
## [46,] 0.7760439 0.71919630 0.424389398
## [47,] 0.7848202 0.67720092 0.176559926
## [48,] 0.4403441 0.63246845 0.018308031
## [49,] 0.7104986 0.68765899 0.026147772
## [50,] 0.8375302 0.78377348 0.309176867
## [51,] 0.2684361 0.60174078 0.079022390
## [52,] 0.8588593 0.85719478 0.050557348
## [53,] 0.4901274 0.33094749 0.078625420
## [54,] 0.3279403 0.24017354 0.053255666
## [55,] 0.6663771 0.42383917 0.100904764
## [56,] 0.7772228 0.49572585 0.032676939
## [57,] 0.7351782 0.90166011 0.005177130
## [58,] 0.7287597 0.57463422 0.008960425
## [59,] 0.7859061 0.71481282 0.081838683
## [60,] 0.7924642 0.52403531 0.139425659
## [61,] 0.5370807 0.58294976 0.072921694
## [62,] 0.6513235 0.71947653 0.314921488
## [63,] 0.4654012 0.73449215 0.008034477
## [64,] 0.3793300 0.78241891 0.024976210
## [65,] 0.3626253 0.71953366 0.050012079
## [66,] 0.6296652 0.36184490 0.144914569
## [67,] 0.7430118 0.65674157 0.054209005
## [68,] 0.9211005 0.41161208 0.036785839
## [69,] 0.6050786 0.62512774 0.073725638
## [70,] 0.4904584 0.35041710 0.061614026
## [71,] 0.6848744 0.31902548 0.040748301
## [72,] 0.4841829 0.42121767 0.351409634
## [73,] 0.3442676 0.47701403 0.031699807
## [74,] 0.4982920 0.07132904 0.068275704
## [75,] 0.5465655 0.51890362 0.162465045
## [76,] 0.5107499 0.07471862 0.391452957
## [77,] 0.7361693 0.97377315 0.004952123
## [78,] 0.5389680 0.70224810 0.029701793
## [79,] 0.2698994 0.65705868 0.023939718
## [80,] 0.4656839 0.92269345 0.024023488
## [81,] 0.8952419 0.78099100 0.133348328
## [82,] 0.7205969 0.94304580 0.070277343
## [83,] 0.9041131 0.13143693 0.145820773
## [84,] 0.9194494 0.67932776 0.081741267
## [85,] 0.4949413 0.64907300 0.065138610
## [86,] 0.5719053 0.41762468 0.108547740
## [87,] 0.4413817 0.37837709 0.056719296
## [88,] 0.8770739 0.76589642 0.153286075
## [89,] 0.6340534 0.92957652 0.096895950
## [90,] 0.1671699 0.28961487 0.061295037
## [91,] 0.7026184 0.41131783 0.067467181
## [92,] 0.4298683 0.12645621 0.073978066
## [93,] 0.7559403 0.73862513 0.154836866
## [94,] 0.4818233 0.10227850 0.155403134
## [95,] 0.6576938 0.36097284 0.004935737
## [96,] 0.8585766 0.64456267 0.089246169
## [97,] 0.7929365 0.22105629 0.128883946
## [98,] 0.6080053 0.88174174 0.181962882
## [99,] 0.4661096 0.84898294 0.132163677
## [100,] 0.6347136 0.60760695 0.007181346
## [101,] 0.8418719 0.32830457 0.043921785
## [102,] 0.3949024 0.18108039 0.259668195
## [103,] 0.5331648 0.60465365 0.103534848
## [104,] 0.5414224 0.57083447 0.173736686
## [105,] 0.4750756 0.41770544 0.075233109
## [106,] 0.7568848 0.70772697 0.132346431
## [107,] 0.7817058 0.57297502 0.365402335
## [108,] 0.5323616 0.62154865 0.182056024
## [109,] 0.5959248 0.29973607 0.131579636
## [110,] 0.3290262 0.42744361 0.183176133
## [111,] 0.6392913 0.54293733 0.036838414
## [112,] 0.4499702 0.81762026 0.119271211
## [113,] 0.6455203 0.44756320 0.097582006
## [114,] 0.4616732 0.63432477 0.042939223
## [115,] 0.5843166 0.21137709 0.091505575
## [116,] 0.4029359 0.34009060 0.014431962
## [117,] 0.7053072 0.79911596 0.056496902
## [118,] 0.8103962 0.33746520 0.173689203
## [119,] 0.6811480 0.93797677 0.099774435
## [120,] 0.8168613 0.84528482 0.037158859
## [121,] 0.6240964 0.87536145 0.064790541
## [122,] 0.2646615 0.75704850 0.010180429
## [123,] 0.3419563 0.89870446 0.024310741
## [124,] 0.8929772 0.60452133 0.105364631
## [125,] 0.5824293 0.37889706 0.070136222
## [126,] 0.5350038 0.60452133 0.104072801
## [127,] 0.6946435 0.77346514 0.153909499
## [128,] 0.5421244 0.97035303 0.005326755
## [129,] 0.5227355 0.36613280 0.027597700
## [130,] 0.6762393 0.61525800 0.232610201
## [131,] 0.8397950 0.33214680 0.340306442
## [132,] 0.7648131 0.32272964 0.150922306
## [133,] 0.3011855 0.67839854 0.186125351
## [134,] 0.6962482 0.03355198 0.049368233
## [135,] 0.1654240 0.13972310 0.010528645
## [136,] 0.7922764 0.66871363 0.150060252
## [137,] 0.7378205 0.29209424 0.461432994
## [138,] 0.7320172 0.46812182 0.241343483
## [139,] 0.5325977 0.80019676 0.185270901
## [140,] 0.5602023 0.56189902 0.035269743
## [141,] 0.2575363 0.37717446 0.124808411
## [142,] 0.4723851 0.55520551 0.076154631
## [143,] 0.7108760 0.07284471 0.131459395
## [144,] 0.7139440 0.68335836 0.024882041
## [145,] 0.6118281 0.49859676 0.164348187
## [146,] 0.4446857 0.60751256 0.063527400
## [147,] 0.2465400 0.83143797 0.092735077
## [148,] 0.4251492 0.86847332 0.144639963
## [149,] 0.5763274 0.39145344 0.042675344
## [150,] 0.8017146 0.58175504 0.057900106
## [151,] 0.7550923 0.30874431 0.084583208
## [152,] 0.5401004 0.61970568 0.187991134
## [153,] 0.4741311 0.14548172 0.401900561
## [154,] 0.7826968 0.95198352 0.100207490
## [155,] 0.4424675 0.44242165 0.095034747
## [156,] 0.6381107 0.81228308 0.131358687
## [157,] 0.1521629 0.35629685 0.029866990
## [158,] 0.4830022 0.35885956 0.087495106
## [159,] 0.7046005 0.56582956 0.051292822
## [160,] 0.5822397 0.83211422 0.091508256
## [161,] 0.4330293 0.48667485 0.066709822
## [162,] 0.4994709 0.20105003 0.072456264
## [163,] 0.5258017 0.64756898 0.221897345
## [164,] 0.8199757 0.86247169 0.051294499
## [165,] 0.6286742 0.75511028 0.379902119
## [166,] 0.7417381 0.11455278 0.121909169
## [167,] 0.5737460 0.81991448 0.086402276
## [168,] 0.5202811 0.15171329 0.452582520
## [169,] 0.8863708 0.91788068 0.166419533
## [170,] 0.4542170 0.63289959 0.316720282
## [171,] 0.6237190 0.61516818 0.132829420
## [172,] 0.4219399 0.78491447 0.114356838
## [173,] 0.5713399 0.77950328 0.042767959
## [174,] 0.7365933 0.83420964 0.085190506
## [175,] 0.6202942 0.40830122 0.021278313
## [176,] 0.7963336 0.54586841 0.065159984
## [177,] 0.7376791 0.48475212 0.376878161
## [178,] 0.7418794 0.69515596 0.430065700
## [179,] 0.5727550 0.37957042 0.437711219
## [180,] 1.0000000 0.67434173 0.072433836
## [181,] 0.5355709 0.63872561 0.231516788
## [182,] 0.8568772 0.60910941 0.055250397
## [183,] 0.8427216 0.68197307 0.242132322
## [184,] 0.3621047 0.65198470 0.121929114
## [185,] 0.7793462 0.37188821 0.097939643
## [186,] 0.4059901 0.47448267 0.192090720
## [187,] 0.5932826 0.68776428 0.175959517
## [188,] 0.8277145 0.20637931 0.202025150
## [189,] 0.7076667 0.31779645 0.137760021
## [190,] 0.5133455 0.84092206 0.094932837
## [191,] 0.7827916 0.79337254 0.105774919
## [192,] 0.3831512 0.62458980 0.034428029
## [193,] 0.4434586 0.67960174 0.100014291
## [194,] 0.5079646 0.85447637 0.140177750
## [195,] 0.7767040 0.47290981 0.581503066
## [196,] 0.6593932 0.40902890 0.246270214
## [197,] 0.6544863 0.79473647 0.117107546
## [198,] 0.8968931 0.67960914 0.188469676
## [199,] 0.3648900 0.53604244 0.020486993
## [200,] 0.6104596 0.60296116 0.162980569
## [201,] 0.2181339 0.76712659 0.209403098
## [202,] 0.3988183 0.89613015 0.099160360
## [203,] 0.7156899 0.61057424 1.000000000
## [204,] 0.6299945 0.25488034 0.011896774
## [205,] 0.6825631 0.68072954 0.219866870
## [206,] 0.3343589 0.82420030 0.111722531
## [207,] 0.5350521 0.36488732 0.036479038
## [208,] 0.2845755 0.69952827 0.025132424
## [209,] 0.6791659 0.66419432 0.039259359
## [210,] 0.4225070 0.72922427 0.053767798
## [211,] 0.6387243 0.67195033 0.020812002
## [212,] 0.2075633 0.77978574 0.011993722
## [213,] 0.5417516 0.40601032 0.095453895
## [214,] 0.1963791 0.60892240 0.097695666
## [215,] 0.7280048 0.50101574 0.024873996
## [216,] 0.3368597 0.48010748 0.008011398
## [217,] 0.5930465 0.77466464 0.015954375
## [218,] 0.5038591 0.44470913 0.042861733
## [219,] 0.5366567 0.73656736 0.232188827
## [220,] 0.5532667 0.23524997 0.008570350
## [221,] 0.3408239 0.36814944 0.026451963
## [222,] 0.8148792 0.27567371 0.064030592
## [223,] 0.5996046 0.23002803 0.048610715
## [224,] 0.6551929 0.38962420 0.047959068
## [225,] 0.3343589 0.69737408 0.049662778
## [226,] 0.7509867 0.84621517 0.016266093
## [227,] 0.7756664 0.67347431 0.097149854
## [228,] 0.4510560 0.64633493 0.025721587
## [229,] 0.4008004 0.41530509 0.134376859
## [230,] 0.4655512 0.83132174 0.020161628
## [231,] 0.4435999 0.84523676 0.013088475
## [232,] 0.5577496 0.65310431 0.013099635
## [233,] 0.5908282 0.54537492 0.040443203
## [234,] 0.8250258 0.41943163 0.125311747
## [235,] 0.5121184 0.69023549 0.012713588
## [236,] 0.5254243 0.58364014 0.022518296
## [237,] 0.6786454 0.55229620 0.012235311
## [238,] 0.5580777 0.08792058 0.034314377
## [239,] 0.7534394 0.48597608 0.034660848
## [240,] 0.4559630 0.31779645 0.018509780
## [241,] 0.7617917 0.93061443 0.081085262
## [242,] 0.6738798 0.58818768 0.015199171
## [243,] 0.5928351 0.73202035 0.008986567
## [244,] 0.5085782 0.91765457 0.019718394
## [245,] 0.4977732 0.53388963 0.031437377
## [246,] 0.5027267 0.57980427 0.008463551
## [247,] 0.2698511 0.70388763 0.033569500
## [248,] 0.4368884 0.23124887 0.379112925
## [249,] 0.6043237 0.00000000 0.034883995
## [250,] 0.4831918 0.92162228 0.004667346
## [251,] 0.4787088 0.57754215 0.033933694
## [252,] 0.5144297 0.74179729 0.034240613
## [253,] 0.7796771 0.66869775 0.090278109
## [254,] 0.4923457 0.04435194 0.062588535
## [255,] 0.3304412 0.85179168 0.019879146
## [256,] 0.4552563 0.78305227 0.007789909
## [257,] 0.5480753 0.65581221 0.038194075
## [258,] 0.5606745 0.77714618 0.015898847
## [259,] 0.4532260 0.55460807 0.126606009
## [260,] 0.4508664 0.61551596 0.028634232
## [261,] 0.4827661 0.23991828 0.012876952
## [262,] 0.6638762 0.30265589 0.011822084
## [263,] 0.6482090 0.62838879 0.015799062
## [264,] 0.3337918 0.52992164 0.056664373
## [265,] 0.6465043 0.62077078 0.129794474
## [266,] 0.5325977 0.67403524 0.028539959
## [267,] 0.8313478 0.79090321 0.066042582
## [268,] 0.5812021 0.61754211 0.117786557
## [269,] 0.6560869 0.81768680 0.116289312
## [270,] 0.3750832 0.74664989 0.026039759
## [271,] 0.5750560 0.14310835 0.037897933
## [272,] 0.5280705 0.93694407 0.005573720
## [273,] 0.1846296 0.89389284 0.008392033
## [274,] 0.6261733 0.10165404 0.033743822
## [275,] 0.5484528 0.73793211 0.300920545
## [276,] 0.5748319 0.40190636 0.013613450
## [277,] 0.7436254 0.71723767 0.006595386
## [278,] 0.6252753 0.77185795 0.020086033
## [279,] 0.6754844 0.45983720 0.019929164
## [280,] 0.5181112 0.45952963 0.054705684
## [281,] 0.6769477 0.60747715 0.383710960
## [282,] 0.5590234 0.81164916 0.150049535
## [283,] 0.6157440 0.29263985 0.121527593
## [284,] 0.6679817 0.53436432 0.018349783
## [285,] 0.7793945 0.54013223 0.010316492
## [286,] 0.7426344 0.65202009 0.043593476
## [287,] 0.7343768 0.13424870 0.097037349
## [288,] 0.8670221 0.11908989 0.033163184
## [289,] 0.3628131 0.79023631 0.043504938
## [290,] 0.5229251 0.40136878 0.027860632
## [291,] 0.6663306 0.55631130 0.046998122
## [292,] 0.6235776 0.77434061 0.007302875
## [293,] 0.5173080 0.37664864 0.037407741
## [294,] 0.5917245 0.37383150 0.031145661
## [295,] 0.7562246 0.81547558 0.041627175
## [296,] 0.6146117 0.65171017 0.013297387
## [297,] 0.5447247 0.57422475 0.060058055
## [298,] 0.5462224 0.57263512 0.034574905
## [299,] 0.8617376 0.77857878 0.038087981
## [300,] 0.6350717 0.49398717 0.011027728
## [301,] 0.6973323 0.60799570 0.038477555
## [302,] 0.7088474 0.75463327 0.026373303
## [303,] 0.5877138 0.84084660 0.007895032
## [304,] 0.5578910 0.66903086 0.017839116
## [305,] 0.3256290 0.28212292 0.048441519
## [306,] 0.3093483 0.23030016 0.051421656
## [307,] 0.4960272 0.89944514 0.037757483
## [308,] 0.5126001 0.99943375 0.007514989
## [309,] 0.7534876 0.48867592 0.135672973
## [310,] 0.3267614 0.74158948 0.008189019
## [311,] 0.6457564 0.74303340 0.059984649
## [312,] 0.5415808 0.44262788 0.084878385
## [313,] 0.6508926 0.46497411 0.011050100
## [314,] 0.3654898 0.46212458 0.022053243
## [315,] 0.2850736 0.81357733 0.027458558
## [316,] 0.4569092 0.63440411 0.032359292
## [317,] 0.5693889 0.25254409 0.102227886
## [318,] 0.7195524 0.75845307 0.027134774
## [319,] 0.9407353 0.76845940 0.002948839
## [320,] 0.7197886 0.45350238 0.005065050
## [321,] 0.5969193 0.53829506 0.005524991
## [322,] 0.2997584 0.74524923 0.004692481
## [323,] 0.8038880 0.37962209 0.019915637
## [324,] 0.7935466 0.32228082 0.008366864
## [325,] 0.5671224 1.00000000 0.007138251
## [326,] 0.7222429 0.83056691 0.019583020
## [327,] 0.3563756 0.22630647 0.098309039
## [328,] 0.2497509 0.54011397 0.002456973
## [329,] 0.7392907 0.27668360 0.181145901
## [330,] 0.7482618 0.27577500 0.175810086
## [331,] 0.4175741 0.61672149 0.002696226
## [332,] 0.7677173 0.60779556 0.013461494
## [333,] 0.4562957 0.96656752 0.009624863
## [334,] 0.8601278 0.77732697 0.004629756
## [335,] 0.4844862 0.98524676 0.001618941
## [336,] 0.6352151 0.46951329 0.029902354
## [337,] 0.5779360 0.41472999 0.009782262
## [338,] 0.6756843 0.48762731 0.027038459
## [339,] 0.8509206 0.67126467 0.000000000
## [340,] 0.4109643 0.62005428 0.078457011
## [341,] 0.5790701 0.25029141 0.055270961
## [342,] 0.9428122 0.55551990 0.017972980
## [343,] 0.5495219 0.20637931 0.013195741
## [344,] 0.0000000 0.05187527 0.021546839
## [345,] 0.9421504 0.86895567 0.020673909
## [346,] 0.7496786 0.25195446 0.013585668
## [347,] 0.7697012 0.51068352 0.022221776
## [348,] 0.6481539 0.64537748 0.013413459
## [349,] 0.4549737 0.41505888 0.088088957
## [350,] 0.8948834 0.59346064 0.069296679
## [351,] 0.6652947 0.83220508 0.005058814
## [352,] 0.7709749 0.26410555 0.001133294
## [353,] 0.4391066 0.98554161 0.005803497
## [354,] 0.5685874 0.69889627 0.012535777
## [355,] 0.7931226 0.51433109 0.202213930
## [356,] 0.7603043 0.21902222 0.080123913
## [357,] 0.5867675 0.88769398 0.005169875
## [358,] 0.4238082 0.76975192 0.017800703
## [359,] 0.4037391 0.47526313 0.008395150
## [360,] 0.6989162 0.30485200 0.687166626
## [361,] 0.6040962 0.59715218 0.011646610
## [362,] 0.8353844 0.54549844 0.016054228
## [363,] 0.6825786 0.68309097 0.106578076
## [364,] 0.6805948 0.56652378 0.219950105
## [365,] 0.6553791 0.68366132 0.096428965
## [366,] 0.6735109 0.78312038 0.002123020
## [367,] 0.4861874 0.41798777 0.046129962
## [368,] 0.5780791 0.63807827 0.017769475
## [369,] 0.4627176 0.29956258 0.108925816
## [370,] 0.2954615 0.42645503 0.027205775
## [371,] 0.7960493 0.10718525 0.108203855
## [372,] 0.6257717 0.66838779 0.036786225
## [373,] 0.5494145 0.82112585 0.011302256
## [374,] 0.3444762 0.74272898 0.339097969
## [375,] 0.5657539 0.79511496 0.008622376
## [376,] 0.6841832 0.49928838 0.007266606
## [377,] 0.4829281 0.61775210 0.028624430
## log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis
## [1,] 2.397400e-01
## [2,] 5.291152e-01
## [3,] 1.567478e-01
## [4,] 3.041208e-01
## [5,] 2.724461e-01
## [6,] 1.011191e-01
## [7,] 9.593697e-02
## [8,] 1.944605e-01
## [9,] 2.995754e-01
## [10,] 7.654610e-02
## [11,] 4.126296e-01
## [12,] 5.193603e-01
## [13,] 2.490286e-01
## [14,] 3.489580e-02
## [15,] 1.514398e-03
## [16,] 5.920399e-01
## [17,] 4.913632e-01
## [18,] 4.875785e-01
## [19,] 4.211767e-01
## [20,] 2.136526e-01
## [21,] 1.664625e-01
## [22,] 2.260464e-01
## [23,] 4.517123e-01
## [24,] 1.346540e-01
## [25,] 1.442558e-01
## [26,] 2.188339e-01
## [27,] 2.571811e-01
## [28,] 1.247451e-01
## [29,] 4.808723e-01
## [30,] 2.910870e-01
## [31,] 5.388238e-02
## [32,] 2.185788e-02
## [33,] 4.150770e-01
## [34,] 5.499199e-01
## [35,] 3.085205e-01
## [36,] 5.559479e-01
## [37,] 5.050481e-01
## [38,] 2.793902e-01
## [39,] 4.170790e-01
## [40,] 1.953189e-01
## [41,] 7.000312e-02
## [42,] 2.085855e-03
## [43,] 2.775968e-01
## [44,] 2.483991e-01
## [45,] 3.887797e-01
## [46,] 4.920587e-01
## [47,] 1.376308e-01
## [48,] 1.907841e-01
## [49,] 5.274347e-01
## [50,] 2.838181e-01
## [51,] 5.953514e-02
## [52,] 6.881742e-01
## [53,] 6.709201e-02
## [54,] 1.211548e-02
## [55,] 1.652594e-01
## [56,] 1.856614e-01
## [57,] 7.218418e-01
## [58,] 3.518289e-01
## [59,] 3.005156e-01
## [60,] 8.642406e-02
## [61,] 2.054432e-01
## [62,] 1.350720e-01
## [63,] 3.826507e-01
## [64,] 3.453309e-01
## [65,] 1.096477e-01
## [66,] 3.086905e-03
## [67,] 1.687649e-01
## [68,] 7.045832e-02
## [69,] 2.985850e-01
## [70,] 1.131773e-01
## [71,] 2.045503e-02
## [72,] 5.677450e-03
## [73,] 6.758801e-02
## [74,] 2.357491e-02
## [75,] 1.022554e-01
## [76,] 5.057729e-02
## [77,] 9.218339e-01
## [78,] 2.478097e-01
## [79,] 2.872986e-01
## [80,] 6.414137e-01
## [81,] 1.899239e-01
## [82,] 6.610884e-01
## [83,] 7.827615e-05
## [84,] 1.391387e-01
## [85,] 2.191260e-01
## [86,] 3.025414e-02
## [87,] 2.287347e-03
## [88,] 3.223380e-01
## [89,] 4.929763e-01
## [90,] 1.835810e-02
## [91,] 1.848928e-01
## [92,] 7.195333e-03
## [93,] 2.029351e-01
## [94,] 3.841140e-04
## [95,] 2.570339e-01
## [96,] 2.473318e-01
## [97,] 1.004562e-03
## [98,] 4.238887e-01
## [99,] 5.570362e-01
## [100,] 2.718868e-01
## [101,] 3.278601e-04
## [102,] 3.489717e-04
## [103,] 2.232722e-01
## [104,] 1.851429e-01
## [105,] 9.627124e-02
## [106,] 2.728411e-01
## [107,] 5.100874e-02
## [108,] 7.162308e-02
## [109,] 5.363783e-02
## [110,] 1.176048e-02
## [111,] 3.303775e-01
## [112,] 2.102012e-01
## [113,] 6.587663e-02
## [114,] 2.077830e-01
## [115,] 1.359483e-03
## [116,] 2.824806e-02
## [117,] 3.527963e-01
## [118,] 3.410911e-03
## [119,] 4.067836e-01
## [120,] 4.350006e-01
## [121,] 6.045596e-01
## [122,] 2.439526e-01
## [123,] 4.951186e-01
## [124,] 2.515508e-01
## [125,] 2.161628e-02
## [126,] 1.595177e-01
## [127,] 3.721971e-01
## [128,] 8.613035e-01
## [129,] 1.791192e-02
## [130,] 1.408448e-01
## [131,] 2.848578e-03
## [132,] 1.427410e-03
## [133,] 2.136499e-01
## [134,] 1.487923e-02
## [135,] 6.184701e-04
## [136,] 1.187272e-01
## [137,] 0.000000e+00
## [138,] 7.094061e-02
## [139,] 3.709356e-01
## [140,] 2.881873e-01
## [141,] 1.628067e-02
## [142,] 3.237272e-02
## [143,] 1.338928e-02
## [144,] 1.336502e-01
## [145,] 7.554039e-02
## [146,] 2.819718e-01
## [147,] 4.387551e-01
## [148,] 3.858402e-01
## [149,] 3.651905e-02
## [150,] 3.069202e-01
## [151,] 4.607124e-03
## [152,] 2.435460e-01
## [153,] 1.504206e-02
## [154,] 5.272012e-01
## [155,] 7.901821e-02
## [156,] 2.804057e-01
## [157,] 1.230529e-01
## [158,] 1.151687e-01
## [159,] 5.151930e-02
## [160,] 6.130944e-01
## [161,] 1.698075e-01
## [162,] 2.898046e-03
## [163,] 2.514117e-01
## [164,] 5.949901e-01
## [165,] 1.013428e-01
## [166,] 1.513188e-02
## [167,] 4.697283e-01
## [168,] 8.059701e-03
## [169,] 3.944200e-01
## [170,] 1.993675e-01
## [171,] 1.948825e-01
## [172,] 3.576480e-01
## [173,] 4.463580e-01
## [174,] 4.223424e-01
## [175,] 1.263616e-02
## [176,] 1.538423e-01
## [177,] 1.071379e-01
## [178,] 1.017812e-01
## [179,] 3.667825e-02
## [180,] 1.014716e-01
## [181,] 1.103114e-01
## [182,] 3.009925e-01
## [183,] 3.526847e-01
## [184,] 4.573997e-01
## [185,] 2.426594e-02
## [186,] 4.155415e-02
## [187,] 2.976011e-01
## [188,] 3.872828e-02
## [189,] 1.198259e-01
## [190,] 5.334325e-01
## [191,] 4.382961e-01
## [192,] 1.599586e-01
## [193,] 2.283939e-01
## [194,] 4.568605e-01
## [195,] 1.998528e-02
## [196,] 5.053100e-02
## [197,] 2.634385e-01
## [198,] 4.264505e-01
## [199,] 2.407548e-01
## [200,] 1.978171e-01
## [201,] 4.193395e-01
## [202,] 7.797785e-01
## [203,] 1.787934e-01
## [204,] 3.949806e-03
## [205,] 3.173122e-01
## [206,] 6.905295e-01
## [207,] 2.297634e-02
## [208,] 2.008643e-01
## [209,] 4.677387e-01
## [210,] 2.384467e-01
## [211,] 2.702735e-01
## [212,] 3.907761e-01
## [213,] 1.223545e-01
## [214,] 3.540233e-01
## [215,] 5.419964e-02
## [216,] 3.315163e-01
## [217,] 4.131084e-01
## [218,] 3.876578e-02
## [219,] 1.802196e-01
## [220,] 9.659762e-03
## [221,] 1.216456e-02
## [222,] 3.260875e-03
## [223,] 7.002829e-02
## [224,] 1.368479e-02
## [225,] 1.682029e-01
## [226,] 4.642230e-01
## [227,] 2.452904e-01
## [228,] 2.288096e-01
## [229,] 9.022151e-02
## [230,] 3.594883e-01
## [231,] 5.326557e-01
## [232,] 2.286626e-01
## [233,] 6.414367e-02
## [234,] 5.085824e-02
## [235,] 4.498145e-01
## [236,] 2.284089e-01
## [237,] 3.322314e-01
## [238,] 1.849684e-02
## [239,] 1.950866e-01
## [240,] 6.940245e-02
## [241,] 1.239279e-01
## [242,] 2.097588e-01
## [243,] 5.041995e-01
## [244,] 7.679753e-01
## [245,] 2.066610e-01
## [246,] 1.556949e-01
## [247,] 4.593429e-01
## [248,] 2.115262e-02
## [249,] 2.292690e-02
## [250,] 7.294126e-01
## [251,] 6.709050e-02
## [252,] 8.129877e-01
## [253,] 6.294558e-01
## [254,] 1.553727e-02
## [255,] 4.978376e-01
## [256,] 4.675506e-01
## [257,] 2.923787e-01
## [258,] 3.573832e-01
## [259,] 5.647727e-02
## [260,] 2.148025e-01
## [261,] 1.812345e-02
## [262,] 9.380474e-02
## [263,] 2.351632e-01
## [264,] 6.682910e-02
## [265,] 2.738489e-01
## [266,] 2.518843e-01
## [267,] 4.182898e-01
## [268,] 2.024770e-01
## [269,] 3.309841e-01
## [270,] 4.746869e-01
## [271,] 4.015234e-03
## [272,] 4.879880e-01
## [273,] 6.676405e-01
## [274,] 1.145126e-02
## [275,] 3.135833e-01
## [276,] 5.305606e-02
## [277,] 7.403577e-01
## [278,] 2.203043e-01
## [279,] 1.080251e-01
## [280,] 4.364861e-02
## [281,] 4.333452e-02
## [282,] 2.453659e-01
## [283,] 4.538464e-04
## [284,] 4.649992e-02
## [285,] 2.993376e-01
## [286,] 2.565996e-01
## [287,] 4.258534e-04
## [288,] 5.672126e-03
## [289,] 2.536460e-01
## [290,] 4.504679e-02
## [291,] 2.280244e-01
## [292,] 5.588489e-01
## [293,] 1.056822e-01
## [294,] 1.856990e-01
## [295,] 7.001604e-01
## [296,] 2.368896e-01
## [297,] 1.428361e-01
## [298,] 3.469638e-01
## [299,] 5.089279e-01
## [300,] 1.371933e-01
## [301,] 2.441394e-01
## [302,] 5.284935e-01
## [303,] 5.205923e-01
## [304,] 2.893199e-01
## [305,] 4.987773e-03
## [306,] 1.704461e-03
## [307,] 2.954046e-01
## [308,] 8.529621e-01
## [309,] 1.084928e-01
## [310,] 3.428337e-01
## [311,] 2.428780e-01
## [312,] 1.626554e-02
## [313,] 5.677641e-02
## [314,] 4.401637e-02
## [315,] 4.481858e-01
## [316,] 8.365445e-02
## [317,] 1.337189e-03
## [318,] 4.222281e-01
## [319,] 3.969112e-01
## [320,] 2.638302e-01
## [321,] 7.707069e-02
## [322,] 7.050127e-01
## [323,] 5.505328e-02
## [324,] 1.414824e-03
## [325,] 1.000000e+00
## [326,] 4.289758e-01
## [327,] 3.942098e-03
## [328,] 3.070228e-01
## [329,] 3.192672e-02
## [330,] 2.975062e-03
## [331,] 2.083828e-01
## [332,] 2.881961e-01
## [333,] 5.529846e-01
## [334,] 5.272508e-01
## [335,] 9.032455e-01
## [336,] 9.825602e-02
## [337,] 1.196114e-01
## [338,] 9.697106e-02
## [339,] 8.506092e-01
## [340,] 1.720951e-01
## [341,] 1.561811e-02
## [342,] 1.649856e-01
## [343,] 7.960680e-03
## [344,] 4.596765e-02
## [345,] 4.588700e-01
## [346,] 1.365568e-02
## [347,] 2.813345e-01
## [348,] 6.685049e-02
## [349,] 1.031654e-01
## [350,] 2.932714e-01
## [351,] 6.846706e-01
## [352,] 8.237691e-02
## [353,] 7.311367e-01
## [354,] 2.619596e-01
## [355,] 1.096851e-01
## [356,] 5.680093e-03
## [357,] 6.549309e-01
## [358,] 2.816295e-01
## [359,] 1.181481e-01
## [360,] 5.448769e-03
## [361,] 3.335111e-01
## [362,] 9.119849e-02
## [363,] 2.585351e-01
## [364,] 1.474603e-01
## [365,] 3.415204e-01
## [366,] 5.461101e-01
## [367,] 3.668564e-02
## [368,] 2.577125e-01
## [369,] 1.899853e-03
## [370,] 1.408147e-01
## [371,] 3.309144e-03
## [372,] 1.941129e-01
## [373,] 4.702332e-01
## [374,] 1.863104e-01
## [375,] 3.965989e-01
## [376,] 1.389836e-01
## [377,] 8.361282e-02
## log.sigma.2.0.mm.3D_glcm_Autocorrelation
## [1,] 0.22739667
## [2,] 0.26702929
## [3,] 0.18690357
## [4,] 0.18787704
## [5,] 0.54090756
## [6,] 0.36830579
## [7,] 0.12301294
## [8,] 0.13160235
## [9,] 0.20771317
## [10,] 0.16458380
## [11,] 0.15648333
## [12,] 0.31232978
## [13,] 0.09792871
## [14,] 0.54566998
## [15,] 0.07590787
## [16,] 0.22685672
## [17,] 0.32311155
## [18,] 0.18749169
## [19,] 0.23699527
## [20,] 0.34325763
## [21,] 0.15331663
## [22,] 0.31337246
## [23,] 0.20152499
## [24,] 0.29994754
## [25,] 0.29944764
## [26,] 0.21705690
## [27,] 0.24704822
## [28,] 1.00000000
## [29,] 0.21142902
## [30,] 0.20956139
## [31,] 0.22491677
## [32,] 0.25005507
## [33,] 0.26862437
## [34,] 0.13027665
## [35,] 0.13954482
## [36,] 0.21797931
## [37,] 0.21916805
## [38,] 0.15040274
## [39,] 0.23991414
## [40,] 0.21205750
## [41,] 0.34321718
## [42,] 0.20518744
## [43,] 0.19855109
## [44,] 0.21195318
## [45,] 0.14307725
## [46,] 0.58087796
## [47,] 0.24235033
## [48,] 0.22904749
## [49,] 0.08664445
## [50,] 0.34453820
## [51,] 0.35390531
## [52,] 0.20483109
## [53,] 0.12186084
## [54,] 0.05881942
## [55,] 0.09729431
## [56,] 0.19204272
## [57,] 0.09675918
## [58,] 0.12418914
## [59,] 0.20661241
## [60,] 0.22290225
## [61,] 0.07774257
## [62,] 0.32079711
## [63,] 0.14087585
## [64,] 0.16961986
## [65,] 0.20798886
## [66,] 0.24116306
## [67,] 0.18434124
## [68,] 0.06264678
## [69,] 0.10090540
## [70,] 0.02836364
## [71,] 0.14212317
## [72,] 0.21954013
## [73,] 0.20299707
## [74,] 0.10212276
## [75,] 0.22898188
## [76,] 0.23427995
## [77,] 0.16323061
## [78,] 0.25853235
## [79,] 0.21053520
## [80,] 0.48889322
## [81,] 0.34863283
## [82,] 0.18646052
## [83,] 0.12241468
## [84,] 0.29298867
## [85,] 0.26479314
## [86,] 0.08809622
## [87,] 0.18251985
## [88,] 0.17563050
## [89,] 0.45515585
## [90,] 0.07686323
## [91,] 0.16839053
## [92,] 0.10459395
## [93,] 0.34615861
## [94,] 0.16912016
## [95,] 0.00000000
## [96,] 0.20274210
## [97,] 0.10396622
## [98,] 0.30910981
## [99,] 0.27312951
## [100,] 0.10985635
## [101,] 0.23455317
## [102,] 0.25674746
## [103,] 0.33229630
## [104,] 0.27349973
## [105,] 0.12887155
## [106,] 0.18597258
## [107,] 0.32252025
## [108,] 0.21974515
## [109,] 0.28392071
## [110,] 0.23446560
## [111,] 0.12795939
## [112,] 0.31443633
## [113,] 0.19826231
## [114,] 0.23637916
## [115,] 0.06853056
## [116,] 0.10386332
## [117,] 0.72374842
## [118,] 0.14498854
## [119,] 0.31290429
## [120,] 0.45131787
## [121,] 0.11905775
## [122,] 0.19339631
## [123,] 0.17048018
## [124,] 0.12926496
## [125,] 0.17019578
## [126,] 0.19731091
## [127,] 0.27811836
## [128,] 0.09370489
## [129,] 0.05962821
## [130,] 0.31740938
## [131,] 0.22936348
## [132,] 0.10354313
## [133,] 0.41414793
## [134,] 0.06629025
## [135,] 0.33294857
## [136,] 0.72582536
## [137,] 0.30202120
## [138,] 0.19327667
## [139,] 0.24769933
## [140,] 0.06086743
## [141,] 0.13166234
## [142,] 0.22553526
## [143,] 0.09006077
## [144,] 0.18242836
## [145,] 0.32823731
## [146,] 0.04996459
## [147,] 0.40445273
## [148,] 0.32198584
## [149,] 0.21056035
## [150,] 0.07865155
## [151,] 0.17688791
## [152,] 0.16626029
## [153,] 0.24363568
## [154,] 0.28428039
## [155,] 0.13137520
## [156,] 0.77016828
## [157,] 0.04228281
## [158,] 0.04249172
## [159,] 0.09286968
## [160,] 0.13557964
## [161,] 0.10955052
## [162,] 0.04996506
## [163,] 0.25656238
## [164,] 0.20377917
## [165,] 0.36312999
## [166,] 0.30592182
## [167,] 0.17727342
## [168,] 0.16860762
## [169,] 0.22419545
## [170,] 0.25901864
## [171,] 0.15196124
## [172,] 0.18716518
## [173,] 0.24575265
## [174,] 0.12430167
## [175,] 0.12101395
## [176,] 0.18604687
## [177,] 0.16782275
## [178,] 0.47105284
## [179,] 0.15112186
## [180,] 0.24553878
## [181,] 0.30166140
## [182,] 0.10163802
## [183,] 0.59299339
## [184,] 0.12635246
## [185,] 0.16160369
## [186,] 0.18396156
## [187,] 0.30814563
## [188,] 0.09111450
## [189,] 0.08715903
## [190,] 0.19943428
## [191,] 0.19021041
## [192,] 0.13857735
## [193,] 0.17498864
## [194,] 0.33953440
## [195,] 0.31853028
## [196,] 0.19615148
## [197,] 0.32884556
## [198,] 0.12840028
## [199,] 0.10752761
## [200,] 0.28162417
## [201,] 0.36851768
## [202,] 0.15799176
## [203,] 0.52337586
## [204,] 0.08317401
## [205,] 0.22882034
## [206,] 0.11905422
## [207,] 0.08099917
## [208,] 0.14949895
## [209,] 0.05371290
## [210,] 0.16684896
## [211,] 0.19247020
## [212,] 0.14924609
## [213,] 0.05658821
## [214,] 0.10506260
## [215,] 0.14960113
## [216,] 0.06886717
## [217,] 0.20363189
## [218,] 0.25869535
## [219,] 0.28786164
## [220,] 0.14042013
## [221,] 0.13711129
## [222,] 0.09705437
## [223,] 0.09628840
## [224,] 0.19595518
## [225,] 0.36078933
## [226,] 0.28821016
## [227,] 0.14543515
## [228,] 0.15578856
## [229,] 0.12696527
## [230,] 0.31282222
## [231,] 0.17913953
## [232,] 0.13672441
## [233,] 0.14032024
## [234,] 0.22216623
## [235,] 0.11050892
## [236,] 0.11454602
## [237,] 0.21057178
## [238,] 0.19354436
## [239,] 0.18417554
## [240,] 0.11916230
## [241,] 0.44707106
## [242,] 0.16007461
## [243,] 0.07630217
## [244,] 0.09731927
## [245,] 0.17205626
## [246,] 0.12751219
## [247,] 0.14129597
## [248,] 0.10385697
## [249,] 0.12398063
## [250,] 0.24864827
## [251,] 0.26154218
## [252,] 0.05343136
## [253,] 0.06269036
## [254,] 0.05238492
## [255,] 0.17133429
## [256,] 0.23582877
## [257,] 0.18875968
## [258,] 0.24312349
## [259,] 0.25782158
## [260,] 0.19571483
## [261,] 0.16692319
## [262,] 0.14256846
## [263,] 0.24742863
## [264,] 0.18307348
## [265,] 0.22474728
## [266,] 0.21165338
## [267,] 0.15478959
## [268,] 0.24783462
## [269,] 0.48994185
## [270,] 0.33423038
## [271,] 0.11762399
## [272,] 0.18382357
## [273,] 0.28712724
## [274,] 0.23934933
## [275,] 0.81127541
## [276,] 0.13456373
## [277,] 0.03416199
## [278,] 0.18098407
## [279,] 0.23108398
## [280,] 0.18445620
## [281,] 0.35055576
## [282,] 0.24982771
## [283,] 0.21967699
## [284,] 0.11205315
## [285,] 0.10506144
## [286,] 0.43111991
## [287,] 0.09292470
## [288,] 0.20924706
## [289,] 0.20123575
## [290,] 0.25399829
## [291,] 0.15539764
## [292,] 0.15470858
## [293,] 0.06531252
## [294,] 0.16463584
## [295,] 0.09187081
## [296,] 0.23655345
## [297,] 0.14614230
## [298,] 0.08148734
## [299,] 0.15171061
## [300,] 0.08086853
## [301,] 0.14731478
## [302,] 0.41066440
## [303,] 0.20685847
## [304,] 0.12045595
## [305,] 0.15927025
## [306,] 0.11222577
## [307,] 0.37887013
## [308,] 0.11333801
## [309,] 0.15388908
## [310,] 0.10387583
## [311,] 0.24996017
## [312,] 0.22697739
## [313,] 0.11449861
## [314,] 0.18126290
## [315,] 0.14527088
## [316,] 0.21706613
## [317,] 0.12584832
## [318,] 0.29061733
## [319,] 0.16308437
## [320,] 0.09323431
## [321,] 0.08391566
## [322,] 0.14119734
## [323,] 0.20134506
## [324,] 0.08727651
## [325,] 0.09608480
## [326,] 0.16636992
## [327,] 0.16200318
## [328,] 0.06134906
## [329,] 0.14722959
## [330,] 0.25337807
## [331,] 0.15915405
## [332,] 0.13762141
## [333,] 0.19373961
## [334,] 0.16612623
## [335,] 0.13946000
## [336,] 0.17760516
## [337,] 0.09901064
## [338,] 0.37803756
## [339,] 0.01762596
## [340,] 0.19133196
## [341,] 0.04786117
## [342,] 0.16866620
## [343,] 0.01250506
## [344,] 0.02017745
## [345,] 0.20726231
## [346,] 0.07552193
## [347,] 0.09837105
## [348,] 0.18310458
## [349,] 0.10814226
## [350,] 0.08690753
## [351,] 0.08990699
## [352,] 0.11722545
## [353,] 0.24600933
## [354,] 0.14527502
## [355,] 0.27026965
## [356,] 0.21876233
## [357,] 0.15403673
## [358,] 0.20051991
## [359,] 0.23074406
## [360,] 0.42630329
## [361,] 0.14375592
## [362,] 0.17188720
## [363,] 0.21526783
## [364,] 0.12427153
## [365,] 0.22586064
## [366,] 0.14796358
## [367,] 0.07872916
## [368,] 0.15227323
## [369,] 0.07551547
## [370,] 0.07142843
## [371,] 0.18607713
## [372,] 0.27243700
## [373,] 0.23062690
## [374,] 0.84416983
## [375,] 0.22256851
## [376,] 0.08275056
## [377,] 0.19429023
## log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis
## [1,] 0.002111614
## [2,] 0.008060090
## [3,] 0.022669071
## [4,] 0.019655183
## [5,] 0.015907643
## [6,] 0.014486345
## [7,] 0.027625905
## [8,] 0.008827445
## [9,] 0.033239580
## [10,] 0.031023070
## [11,] 0.014023152
## [12,] 0.010918874
## [13,] 0.020309752
## [14,] 0.116756888
## [15,] 0.019102911
## [16,] 0.020832415
## [17,] 0.024626911
## [18,] 0.041817827
## [19,] 0.016816147
## [20,] 0.013294692
## [21,] 0.015891545
## [22,] 0.041309901
## [23,] 0.039565569
## [24,] 0.050932813
## [25,] 0.011065685
## [26,] 0.056592763
## [27,] 0.025650463
## [28,] 0.000000000
## [29,] 0.041145742
## [30,] 0.003955652
## [31,] 0.041158642
## [32,] 0.012835296
## [33,] 0.039860982
## [34,] 0.016192171
## [35,] 0.035884658
## [36,] 0.024432033
## [37,] 0.016638641
## [38,] 0.063426415
## [39,] 0.010322188
## [40,] 0.010346201
## [41,] 0.009799663
## [42,] 0.026753757
## [43,] 0.079981619
## [44,] 0.024673389
## [45,] 0.036706097
## [46,] 0.048808043
## [47,] 0.013282673
## [48,] 0.037785259
## [49,] 0.039177288
## [50,] 0.010790333
## [51,] 0.031002540
## [52,] 0.022569207
## [53,] 0.227949996
## [54,] 0.088896794
## [55,] 0.042408424
## [56,] 0.028296937
## [57,] 0.034577759
## [58,] 0.042204535
## [59,] 0.045948690
## [60,] 0.024210490
## [61,] 0.017151802
## [62,] 0.011999755
## [63,] 0.035188155
## [64,] 0.014148173
## [65,] 0.035590157
## [66,] 0.043493717
## [67,] 0.015003050
## [68,] 0.026730818
## [69,] 0.078958004
## [70,] 0.055826889
## [71,] 0.016657962
## [72,] 0.016699929
## [73,] 0.016136206
## [74,] 0.716496209
## [75,] 0.017908296
## [76,] 0.058084137
## [77,] 0.048416683
## [78,] 0.036804077
## [79,] 0.016275385
## [80,] 0.063381512
## [81,] 0.023951967
## [82,] 0.017957703
## [83,] 0.044669961
## [84,] 0.037081342
## [85,] 0.039229696
## [86,] 0.010055429
## [87,] 0.013434582
## [88,] 0.020462196
## [89,] 0.037535925
## [90,] 0.031362705
## [91,] 0.044818293
## [92,] 0.106992972
## [93,] 0.017204633
## [94,] 0.057675287
## [95,] 0.435531210
## [96,] 0.014318363
## [97,] 0.021649412
## [98,] 0.013358747
## [99,] 0.057156523
## [100,] 0.046205176
## [101,] 0.044506422
## [102,] 0.039925309
## [103,] 0.032204952
## [104,] 0.031188819
## [105,] 0.030253948
## [106,] 0.023589750
## [107,] 0.012030537
## [108,] 0.030745634
## [109,] 0.049846520
## [110,] 0.021099267
## [111,] 0.116518960
## [112,] 0.022278032
## [113,] 0.019659994
## [114,] 0.024720169
## [115,] 0.036852915
## [116,] 0.036728531
## [117,] 0.010641715
## [118,] 0.014514825
## [119,] 0.010628116
## [120,] 0.034156867
## [121,] 0.047551060
## [122,] 0.006800824
## [123,] 0.010659449
## [124,] 0.029360549
## [125,] 0.015160108
## [126,] 0.027972876
## [127,] 0.019744108
## [128,] 0.071110506
## [129,] 0.014004982
## [130,] 0.047910134
## [131,] 0.026086586
## [132,] 0.010309808
## [133,] 0.021191442
## [134,] 0.464613527
## [135,] 0.311201632
## [136,] 0.014010905
## [137,] 0.043632718
## [138,] 0.049900785
## [139,] 0.059152444
## [140,] 0.022685744
## [141,] 0.017124414
## [142,] 0.041219566
## [143,] 0.096472763
## [144,] 0.011745326
## [145,] 0.020396877
## [146,] 0.028837925
## [147,] 0.068668926
## [148,] 0.009452756
## [149,] 0.035473544
## [150,] 0.038994803
## [151,] 0.024443529
## [152,] 0.010941888
## [153,] 0.051393586
## [154,] 0.016896374
## [155,] 0.012705552
## [156,] 0.059309263
## [157,] 0.038813345
## [158,] 0.339231253
## [159,] 0.002633977
## [160,] 0.030021829
## [161,] 0.027637033
## [162,] 0.028119109
## [163,] 0.035602699
## [164,] 0.027761837
## [165,] 0.006349109
## [166,] 1.000000000
## [167,] 0.028776840
## [168,] 0.061462170
## [169,] 0.013476808
## [170,] 0.007386141
## [171,] 0.020383786
## [172,] 0.028211357
## [173,] 0.031246417
## [174,] 0.023823819
## [175,] 0.034597778
## [176,] 0.013049889
## [177,] 0.012772486
## [178,] 0.015712113
## [179,] 0.045715848
## [180,] 0.016657918
## [181,] 0.006714941
## [182,] 0.008771833
## [183,] 0.030683401
## [184,] 0.035682066
## [185,] 0.019736704
## [186,] 0.014770991
## [187,] 0.074812257
## [188,] 0.028096134
## [189,] 0.029643760
## [190,] 0.020807495
## [191,] 0.028260439
## [192,] 0.039658361
## [193,] 0.019591363
## [194,] 0.041603832
## [195,] 0.029288500
## [196,] 0.060286233
## [197,] 0.027467311
## [198,] 0.034113631
## [199,] 0.033302033
## [200,] 0.041181187
## [201,] 0.014281465
## [202,] 0.034310299
## [203,] 0.030845312
## [204,] 0.027011136
## [205,] 0.093044293
## [206,] 0.050205517
## [207,] 0.020017372
## [208,] 0.008354282
## [209,] 0.220086462
## [210,] 0.007989875
## [211,] 0.009326443
## [212,] 0.016029716
## [213,] 0.012822717
## [214,] 0.066099828
## [215,] 0.058897597
## [216,] 0.056602587
## [217,] 0.025298304
## [218,] 0.017870573
## [219,] 0.013116664
## [220,] 0.038951646
## [221,] 0.022186217
## [222,] 0.031536962
## [223,] 0.098187824
## [224,] 0.009484911
## [225,] 0.037313514
## [226,] 0.023098578
## [227,] 0.017908988
## [228,] 0.206594198
## [229,] 0.034333813
## [230,] 0.021856175
## [231,] 0.072542713
## [232,] 0.024921456
## [233,] 0.012428351
## [234,] 0.053837521
## [235,] 0.140219845
## [236,] 0.053117098
## [237,] 0.055257898
## [238,] 0.102982394
## [239,] 0.029569387
## [240,] 0.049618040
## [241,] 0.004748388
## [242,] 0.029951842
## [243,] 0.087348137
## [244,] 0.050171920
## [245,] 0.194142225
## [246,] 0.039905857
## [247,] 0.083280758
## [248,] 0.035384841
## [249,] 0.060687276
## [250,] 0.039447594
## [251,] 0.022848826
## [252,] 0.090483798
## [253,] 0.108191822
## [254,] 0.446564630
## [255,] 0.029706398
## [256,] 0.028658331
## [257,] 0.024617960
## [258,] 0.034348330
## [259,] 0.016107978
## [260,] 0.023937512
## [261,] 0.094290346
## [262,] 0.043552847
## [263,] 0.017094815
## [264,] 0.008680453
## [265,] 0.058345794
## [266,] 0.068594804
## [267,] 0.031242806
## [268,] 0.028377687
## [269,] 0.160954450
## [270,] 0.062925785
## [271,] 0.126713113
## [272,] 0.014795438
## [273,] 0.080140053
## [274,] 0.150997140
## [275,] 0.035307841
## [276,] 0.024918256
## [277,] 0.276593160
## [278,] 0.006278457
## [279,] 0.017118402
## [280,] 0.070819397
## [281,] 0.018780845
## [282,] 0.028698717
## [283,] 0.019818668
## [284,] 0.019117395
## [285,] 0.086683720
## [286,] 0.060009772
## [287,] 0.079848690
## [288,] 0.042768063
## [289,] 0.023782789
## [290,] 0.028351006
## [291,] 0.029238887
## [292,] 0.040141606
## [293,] 0.018576283
## [294,] 0.052199889
## [295,] 0.054187292
## [296,] 0.030644151
## [297,] 0.024233381
## [298,] 0.104063598
## [299,] 0.058656835
## [300,] 0.031659970
## [301,] 0.016498661
## [302,] 0.036240609
## [303,] 0.028874231
## [304,] 0.056101072
## [305,] 0.023470063
## [306,] 0.034800330
## [307,] 0.020086595
## [308,] 0.027443214
## [309,] 0.034956561
## [310,] 0.021593772
## [311,] 0.021110223
## [312,] 0.021395658
## [313,] 0.019646097
## [314,] 0.032333225
## [315,] 0.026798757
## [316,] 0.043022469
## [317,] 0.022447576
## [318,] 0.029689885
## [319,] 0.041853975
## [320,] 0.108703371
## [321,] 0.017919921
## [322,] 0.393831961
## [323,] 0.070481699
## [324,] 0.020310092
## [325,] 0.056186716
## [326,] 0.022685126
## [327,] 0.042557319
## [328,] 0.044887389
## [329,] 0.489664966
## [330,] 0.083108586
## [331,] 0.020857524
## [332,] 0.035273046
## [333,] 0.008737958
## [334,] 0.045970933
## [335,] 0.065786919
## [336,] 0.079059920
## [337,] 0.012285705
## [338,] 0.040583911
## [339,] 0.419546405
## [340,] 0.015997628
## [341,] 0.050332557
## [342,] 0.026989021
## [343,] 0.030213259
## [344,] 0.049319995
## [345,] 0.017398991
## [346,] 0.040844375
## [347,] 0.035495092
## [348,] 0.009040554
## [349,] 0.073289363
## [350,] 0.035792302
## [351,] 0.064611689
## [352,] 0.149082849
## [353,] 0.056770699
## [354,] 0.019225914
## [355,] 0.084213745
## [356,] 0.070769208
## [357,] 0.017302589
## [358,] 0.034940283
## [359,] 0.044041286
## [360,] 0.044293083
## [361,] 0.043627540
## [362,] 0.019800361
## [363,] 0.120785943
## [364,] 0.020981750
## [365,] 0.046117324
## [366,] 0.039243942
## [367,] 0.016673146
## [368,] 0.030801183
## [369,] 0.028168105
## [370,] 0.031420915
## [371,] 0.061849514
## [372,] 0.023571114
## [373,] 0.022908570
## [374,] 0.010168379
## [375,] 0.038461027
## [376,] 0.015292222
## [377,] 0.011627713
## wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis
## [1,] 0.708030364
## [2,] 0.833237597
## [3,] 0.484630566
## [4,] 0.667604219
## [5,] 0.828654917
## [6,] 0.564357634
## [7,] 0.445906906
## [8,] 0.548835444
## [9,] 0.677169945
## [10,] 0.527538110
## [11,] 0.586254277
## [12,] 0.747084250
## [13,] 0.511034502
## [14,] 0.255040132
## [15,] 0.254401139
## [16,] 0.696936178
## [17,] 0.725956061
## [18,] 0.823186173
## [19,] 0.749336912
## [20,] 0.638011935
## [21,] 0.449008902
## [22,] 0.673969118
## [23,] 0.793576943
## [24,] 0.646840496
## [25,] 0.786770485
## [26,] 0.606126520
## [27,] 0.835600745
## [28,] 0.890880957
## [29,] 0.751645127
## [30,] 0.693878002
## [31,] 0.698860412
## [32,] 0.313895722
## [33,] 0.763241846
## [34,] 0.781655491
## [35,] 0.630767551
## [36,] 0.928200664
## [37,] 0.871688927
## [38,] 0.546487227
## [39,] 0.746042444
## [40,] 0.508019752
## [41,] 0.591756992
## [42,] 0.161782822
## [43,] 0.388236788
## [44,] 0.723626507
## [45,] 0.614693187
## [46,] 0.801813048
## [47,] 0.653965420
## [48,] 0.642040268
## [49,] 0.622901001
## [50,] 0.530042238
## [51,] 0.672103014
## [52,] 0.757605031
## [53,] 0.165330219
## [54,] 0.348141810
## [55,] 0.496146377
## [56,] 0.516920432
## [57,] 0.915001005
## [58,] 0.561347718
## [59,] 0.568886344
## [60,] 0.529252255
## [61,] 0.542165629
## [62,] 0.535133029
## [63,] 0.718386748
## [64,] 0.731863373
## [65,] 0.769035651
## [66,] 0.324743896
## [67,] 0.616576568
## [68,] 0.361879030
## [69,] 0.614337943
## [70,] 0.339662812
## [71,] 0.431623472
## [72,] 0.375742323
## [73,] 0.525273780
## [74,] 0.003322634
## [75,] 0.492741296
## [76,] 0.055510453
## [77,] 0.945066085
## [78,] 0.671055240
## [79,] 0.678696697
## [80,] 0.949969454
## [81,] 0.730515133
## [82,] 0.821739826
## [83,] 0.106216924
## [84,] 0.631538350
## [85,] 0.658063405
## [86,] 0.309029068
## [87,] 0.316540138
## [88,] 0.748204147
## [89,] 0.806822877
## [90,] 0.265503871
## [91,] 0.617069483
## [92,] 0.298952640
## [93,] 0.677040623
## [94,] 0.111178031
## [95,] 0.553887559
## [96,] 0.611642063
## [97,] 0.200368318
## [98,] 0.761437266
## [99,] 0.781011389
## [100,] 0.685420033
## [101,] 0.323880101
## [102,] 0.116026312
## [103,] 0.627648794
## [104,] 0.614963465
## [105,] 0.358932195
## [106,] 0.656278426
## [107,] 0.556969511
## [108,] 0.512845017
## [109,] 0.220984971
## [110,] 0.345710749
## [111,] 0.613096656
## [112,] 0.729724884
## [113,] 0.418885809
## [114,] 0.696574403
## [115,] 0.228963610
## [116,] 0.468256170
## [117,] 0.786861876
## [118,] 0.255069970
## [119,] 0.900932261
## [120,] 0.785209199
## [121,] 0.670167916
## [122,] 0.720057725
## [123,] 0.768910519
## [124,] 0.545080276
## [125,] 0.448400670
## [126,] 0.586671378
## [127,] 0.726721079
## [128,] 0.908142568
## [129,] 0.408851507
## [130,] 0.650879146
## [131,] 0.225045473
## [132,] 0.337539927
## [133,] 0.666273462
## [134,] 0.022065546
## [135,] 0.096993305
## [136,] 0.676947584
## [137,] 0.253802554
## [138,] 0.448631341
## [139,] 0.765793331
## [140,] 0.484168977
## [141,] 0.342897121
## [142,] 0.566968923
## [143,] 0.117378168
## [144,] 0.670296378
## [145,] 0.227028602
## [146,] 0.484797085
## [147,] 0.719929606
## [148,] 0.838474033
## [149,] 0.353633827
## [150,] 0.553004572
## [151,] 0.251108822
## [152,] 0.694269963
## [153,] 0.136467516
## [154,] 0.888154865
## [155,] 0.287619462
## [156,] 0.773538209
## [157,] 0.364918020
## [158,] 0.361595477
## [159,] 0.579156205
## [160,] 0.677146071
## [161,] 0.515018300
## [162,] 0.163049087
## [163,] 0.522761267
## [164,] 0.793264309
## [165,] 0.592285885
## [166,] 0.022694683
## [167,] 0.670859482
## [168,] 0.128667951
## [169,] 0.664068319
## [170,] 0.487754683
## [171,] 0.522858108
## [172,] 0.730730028
## [173,] 0.779174052
## [174,] 0.703505409
## [175,] 0.355691524
## [176,] 0.500981460
## [177,] 0.416640556
## [178,] 0.573047488
## [179,] 0.066536926
## [180,] 0.637321771
## [181,] 0.508696988
## [182,] 0.540940308
## [183,] 0.743941705
## [184,] 0.482370560
## [185,] 0.351245280
## [186,] 0.332355805
## [187,] 0.615504223
## [188,] 0.123568003
## [189,] 0.233205563
## [190,] 0.762846647
## [191,] 0.635988711
## [192,] 0.674393383
## [193,] 0.587903825
## [194,] 0.644604240
## [195,] 0.543977320
## [196,] 0.449494625
## [197,] 0.699074568
## [198,] 0.570285625
## [199,] 0.539879362
## [200,] 0.574221742
## [201,] 0.548914317
## [202,] 0.669802403
## [203,] 0.738863450
## [204,] 0.255390158
## [205,] 0.645793773
## [206,] 0.597248869
## [207,] 0.336588992
## [208,] 0.611811260
## [209,] 0.671461738
## [210,] 0.622974660
## [211,] 0.622344844
## [212,] 0.760807365
## [213,] 0.327941567
## [214,] 0.654050735
## [215,] 0.505491478
## [216,] 0.506506896
## [217,] 0.804437922
## [218,] 0.393441764
## [219,] 0.639922501
## [220,] 0.190727727
## [221,] 0.366512271
## [222,] 0.290087726
## [223,] 0.219773398
## [224,] 0.340486420
## [225,] 0.700237683
## [226,] 0.753322542
## [227,] 0.620260903
## [228,] 0.671303031
## [229,] 0.402341537
## [230,] 0.812931049
## [231,] 0.709988612
## [232,] 0.577752281
## [233,] 0.454995447
## [234,] 0.336597087
## [235,] 0.540848895
## [236,] 0.596721254
## [237,] 0.523397015
## [238,] 0.157355893
## [239,] 0.474305103
## [240,] 0.435985606
## [241,] 0.813290422
## [242,] 0.572525262
## [243,] 0.610976227
## [244,] 0.838019789
## [245,] 0.609839574
## [246,] 0.673474213
## [247,] 0.606807483
## [248,] 0.174753986
## [249,] 0.000000000
## [250,] 0.905719030
## [251,] 0.460735014
## [252,] 0.589162900
## [253,] 0.564950195
## [254,] 0.028852505
## [255,] 0.745055655
## [256,] 0.784778772
## [257,] 0.669505204
## [258,] 0.759164115
## [259,] 0.474746799
## [260,] 0.559664555
## [261,] 0.311188595
## [262,] 0.383054527
## [263,] 0.555881937
## [264,] 0.457896959
## [265,] 0.659891558
## [266,] 0.618720175
## [267,] 0.772075867
## [268,] 0.602325728
## [269,] 0.729751608
## [270,] 0.579326725
## [271,] 0.158688470
## [272,] 0.885878989
## [273,] 0.765774755
## [274,] 0.010421167
## [275,] 0.755569365
## [276,] 0.449390336
## [277,] 0.645197316
## [278,] 0.821288391
## [279,] 0.456796757
## [280,] 0.434508703
## [281,] 0.622658514
## [282,] 0.725472106
## [283,] 0.271703105
## [284,] 0.535119503
## [285,] 0.623634933
## [286,] 0.634899386
## [287,] 0.126409063
## [288,] 0.075767908
## [289,] 0.843290489
## [290,] 0.387234534
## [291,] 0.529813024
## [292,] 0.735326137
## [293,] 0.249717535
## [294,] 0.321605060
## [295,] 0.782054842
## [296,] 0.690847636
## [297,] 0.552185155
## [298,] 0.514424458
## [299,] 0.725571232
## [300,] 0.386677304
## [301,] 0.455963352
## [302,] 0.866333717
## [303,] 0.784224707
## [304,] 0.602092755
## [305,] 0.184034859
## [306,] 0.102344129
## [307,] 0.770146493
## [308,] 0.782659713
## [309,] 0.429325571
## [310,] 0.668102832
## [311,] 0.674144632
## [312,] 0.425628655
## [313,] 0.519421284
## [314,] 0.483942620
## [315,] 0.759672793
## [316,] 0.666464551
## [317,] 0.261748701
## [318,] 0.656474562
## [319,] 0.835922163
## [320,] 0.457608611
## [321,] 0.475772210
## [322,] 0.694621383
## [323,] 0.497497726
## [324,] 0.340195694
## [325,] 0.809872896
## [326,] 0.732003459
## [327,] 0.245062521
## [328,] 0.625070462
## [329,] 0.364430784
## [330,] 0.063122664
## [331,] 0.602529495
## [332,] 0.564608097
## [333,] 0.864361081
## [334,] 0.793973209
## [335,] 1.000000000
## [336,] 0.520468307
## [337,] 0.388750718
## [338,] 0.599611505
## [339,] 0.715937502
## [340,] 0.643069595
## [341,] 0.222255641
## [342,] 0.553303885
## [343,] 0.210986244
## [344,] 0.048157577
## [345,] 0.892753440
## [346,] 0.253039723
## [347,] 0.457029800
## [348,] 0.612990263
## [349,] 0.402683038
## [350,] 0.431251277
## [351,] 0.798049295
## [352,] 0.361976404
## [353,] 0.934339593
## [354,] 0.617460766
## [355,] 0.482663832
## [356,] 0.118931159
## [357,] 0.884116480
## [358,] 0.719493775
## [359,] 0.515110144
## [360,] 0.580377361
## [361,] 0.581962852
## [362,] 0.540280119
## [363,] 0.703160952
## [364,] 0.558727310
## [365,] 0.729536214
## [366,] 0.680288247
## [367,] 0.386494685
## [368,] 0.730407489
## [369,] 0.303094834
## [370,] 0.418442411
## [371,] 0.094757733
## [372,] 0.706953396
## [373,] 0.835274398
## [374,] 0.890138328
## [375,] 0.744565047
## [376,] 0.517717223
## [377,] 0.617768810
## wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis
## [1,] 0.0108936050
## [2,] 0.0489698361
## [3,] 0.1379592773
## [4,] 0.0846018522
## [5,] 0.1193476738
## [6,] 0.1316047040
## [7,] 0.0039720309
## [8,] 0.1368122794
## [9,] 0.0673058363
## [10,] 0.0864764049
## [11,] 0.1310751779
## [12,] 0.1021062949
## [13,] 0.0607940012
## [14,] 0.1730675148
## [15,] 0.0927139739
## [16,] 0.0535390623
## [17,] 0.1287601548
## [18,] 0.0546028686
## [19,] 0.0658826838
## [20,] 0.0627301959
## [21,] 0.0380209057
## [22,] 0.0954397810
## [23,] 0.0335230896
## [24,] 0.0208785225
## [25,] 0.0507453966
## [26,] 0.0793968199
## [27,] 0.0144257128
## [28,] 0.2470815179
## [29,] 0.0095569850
## [30,] 0.0167012393
## [31,] 0.0560119133
## [32,] 0.1077343808
## [33,] 0.0584996959
## [34,] 0.0112925005
## [35,] 0.0945664971
## [36,] 0.0877404768
## [37,] 0.0302884512
## [38,] 0.0324709141
## [39,] 0.0237251244
## [40,] 0.0686173951
## [41,] 0.0861735514
## [42,] 0.1737702680
## [43,] 0.0675050943
## [44,] 0.0737637678
## [45,] 0.0755423740
## [46,] 0.0954464451
## [47,] 0.0577965152
## [48,] 0.0208334046
## [49,] 0.0312171640
## [50,] 0.1314597456
## [51,] 0.0358571812
## [52,] 0.0486497665
## [53,] 0.0210813702
## [54,] 0.0206257782
## [55,] 0.0424310860
## [56,] 0.0207023099
## [57,] 0.0164408039
## [58,] 0.0080266105
## [59,] 0.0469331332
## [60,] 0.0573601308
## [61,] 0.0743057244
## [62,] 0.2135405649
## [63,] 0.0193715730
## [64,] 0.0216658676
## [65,] 0.0204419870
## [66,] 0.0459583833
## [67,] 0.0259186597
## [68,] 0.0172569292
## [69,] 0.0503720341
## [70,] 0.0270760214
## [71,] 0.0170402037
## [72,] 0.1272123141
## [73,] 0.0160281379
## [74,] 0.0269909141
## [75,] 0.0653971782
## [76,] 0.0762224413
## [77,] 0.0120593758
## [78,] 0.0250491073
## [79,] 0.0177471853
## [80,] 0.0166331481
## [81,] 0.0452806958
## [82,] 0.0837195107
## [83,] 0.0305942299
## [84,] 0.0589861748
## [85,] 0.0470297466
## [86,] 0.0519686879
## [87,] 0.0195764960
## [88,] 0.0264329928
## [89,] 0.0644982446
## [90,] 0.0314008245
## [91,] 0.0349860272
## [92,] 0.0271169232
## [93,] 0.1038673962
## [94,] 0.0459030686
## [95,] 0.0025515174
## [96,] 0.0470626704
## [97,] 0.0342882704
## [98,] 0.1280080873
## [99,] 0.1492687022
## [100,] 0.0123333117
## [101,] 0.0155298066
## [102,] 0.1360628467
## [103,] 0.0536666430
## [104,] 0.0659931270
## [105,] 0.0400651584
## [106,] 0.0771927517
## [107,] 0.1293385223
## [108,] 0.1026563194
## [109,] 0.0424502825
## [110,] 0.0656145279
## [111,] 0.0325753922
## [112,] 0.0670278501
## [113,] 0.0561536386
## [114,] 0.0411823701
## [115,] 0.0327266017
## [116,] 0.0055582000
## [117,] 0.0389395437
## [118,] 0.0677133401
## [119,] 0.0698381857
## [120,] 0.0470715068
## [121,] 0.0542636166
## [122,] 0.0105520255
## [123,] 0.0147820266
## [124,] 0.0523554406
## [125,] 0.0202517504
## [126,] 0.0440489604
## [127,] 0.0866638385
## [128,] 0.0100697513
## [129,] 0.0148202451
## [130,] 0.1112017310
## [131,] 0.1294308236
## [132,] 0.0592737811
## [133,] 0.0561590713
## [134,] 0.0153119187
## [135,] 0.0046876539
## [136,] 0.0846041797
## [137,] 0.1822951399
## [138,] 0.0859308262
## [139,] 0.0884508790
## [140,] 0.0207574343
## [141,] 0.0490060301
## [142,] 0.0401858889
## [143,] 0.0368437134
## [144,] 0.0207910054
## [145,] 0.0427011905
## [146,] 0.0491013815
## [147,] 0.0995253949
## [148,] 0.1430896755
## [149,] 0.0106960907
## [150,] 0.0266710082
## [151,] 0.0326406349
## [152,] 0.0703873663
## [153,] 0.1581932483
## [154,] 0.0930545586
## [155,] 0.0547857223
## [156,] 0.1266462467
## [157,] 0.0140828891
## [158,] 0.0406823868
## [159,] 0.0213408678
## [160,] 0.0564450345
## [161,] 0.0310528187
## [162,] 0.0287678110
## [163,] 0.1284921174
## [164,] 0.0542723462
## [165,] 0.2033808393
## [166,] 0.0410052733
## [167,] 0.0649874222
## [168,] 0.1048620136
## [169,] 0.1372594656
## [170,] 0.1423558370
## [171,] 0.0839195301
## [172,] 0.0678883168
## [173,] 0.0237654801
## [174,] 0.0693883045
## [175,] 0.0098388097
## [176,] 0.0425143277
## [177,] 0.1151989828
## [178,] 0.1213532586
## [179,] 0.1707060704
## [180,] 0.0255723182
## [181,] 0.0893926891
## [182,] 0.0355947365
## [183,] 0.1379148687
## [184,] 0.0449542499
## [185,] 0.0438544015
## [186,] 0.1056659488
## [187,] 0.1024068661
## [188,] 0.0979576750
## [189,] 0.0779198019
## [190,] 0.1258199049
## [191,] 0.0903113704
## [192,] 0.0187505135
## [193,] 0.0828386799
## [194,] 0.0905477752
## [195,] 0.1633844219
## [196,] 0.1203786639
## [197,] 0.0730517588
## [198,] 0.0879076654
## [199,] 0.0223085405
## [200,] 0.0622820803
## [201,] 0.1182738361
## [202,] 0.0749972511
## [203,] 1.0000000000
## [204,] 0.0026496023
## [205,] 0.1156793092
## [206,] 0.0659175154
## [207,] 0.0169655775
## [208,] 0.0195716930
## [209,] 0.0193954830
## [210,] 0.0278940049
## [211,] 0.0126129693
## [212,] 0.0098946600
## [213,] 0.0433588788
## [214,] 0.0572373901
## [215,] 0.0119257474
## [216,] 0.0072781715
## [217,] 0.0115353325
## [218,] 0.0154496895
## [219,] 0.1137966998
## [220,] 0.0049045388
## [221,] 0.0124291167
## [222,] 0.0381446876
## [223,] 0.0187654237
## [224,] 0.0228005912
## [225,] 0.0222342348
## [226,] 0.0194575667
## [227,] 0.0660241255
## [228,] 0.0079846421
## [229,] 0.0555505248
## [230,] 0.0199302791
## [231,] 0.0116367877
## [232,] 0.0117250676
## [233,] 0.0116733703
## [234,] 0.0440990998
## [235,] 0.0133258215
## [236,] 0.0094822526
## [237,] 0.0131656599
## [238,] 0.0123167031
## [239,] 0.0126492515
## [240,] 0.0084366442
## [241,] 0.0185290569
## [242,] 0.0061748328
## [243,] 0.0072653690
## [244,] 0.0123454836
## [245,] 0.0154463795
## [246,] 0.0067103366
## [247,] 0.0176395370
## [248,] 0.1388990126
## [249,] 0.0146232377
## [250,] 0.0038224338
## [251,] 0.0088502701
## [252,] 0.0361568755
## [253,] 0.0456731555
## [254,] 0.0141531603
## [255,] 0.0149232897
## [256,] 0.0042032966
## [257,] 0.0360074061
## [258,] 0.0163939532
## [259,] 0.0270395868
## [260,] 0.0126768349
## [261,] 0.0023854256
## [262,] 0.0056336199
## [263,] 0.0075515234
## [264,] 0.0214560698
## [265,] 0.0806568838
## [266,] 0.0107713223
## [267,] 0.0582973063
## [268,] 0.0998363920
## [269,] 0.0318929021
## [270,] 0.0150253092
## [271,] 0.0122120404
## [272,] 0.0103364822
## [273,] 0.0048033334
## [274,] 0.0115263235
## [275,] 0.1096225019
## [276,] 0.0050149567
## [277,] 0.0084738402
## [278,] 0.0077250663
## [279,] 0.0091529207
## [280,] 0.0182298772
## [281,] 0.1605182531
## [282,] 0.0361824312
## [283,] 0.0293338342
## [284,] 0.0078220004
## [285,] 0.0050155106
## [286,] 0.0177621231
## [287,] 0.0172692445
## [288,] 0.0110474333
## [289,] 0.0159369756
## [290,] 0.0091495447
## [291,] 0.0149869302
## [292,] 0.0056702843
## [293,] 0.0165213485
## [294,] 0.0071467644
## [295,] 0.0149573228
## [296,] 0.0087008870
## [297,] 0.0219749681
## [298,] 0.0220276746
## [299,] 0.0108958654
## [300,] 0.0088252946
## [301,] 0.0244909524
## [302,] 0.0247265179
## [303,] 0.0192945121
## [304,] 0.0161872978
## [305,] 0.0114833948
## [306,] 0.0113096546
## [307,] 0.0148464934
## [308,] 0.0115149942
## [309,] 0.0866987826
## [310,] 0.0069659842
## [311,] 0.0177931677
## [312,] 0.0214806886
## [313,] 0.0030111613
## [314,] 0.0072911459
## [315,] 0.0194184236
## [316,] 0.0108036762
## [317,] 0.0169088715
## [318,] 0.0101235709
## [319,] 0.0021678368
## [320,] 0.0022068840
## [321,] 0.0028536975
## [322,] 0.0035403642
## [323,] 0.0085880753
## [324,] 0.0021766041
## [325,] 0.0049184558
## [326,] 0.0061494337
## [327,] 0.0192591335
## [328,] 0.0052340366
## [329,] 0.0639960360
## [330,] 0.0281143242
## [331,] 0.0019730141
## [332,] 0.0093556575
## [333,] 0.0086282781
## [334,] 0.0024910996
## [335,] 0.0021018252
## [336,] 0.0079693685
## [337,] 0.0035189541
## [338,] 0.0083575994
## [339,] 0.0009931837
## [340,] 0.0090728742
## [341,] 0.0195709709
## [342,] 0.0056559733
## [343,] 0.0074914757
## [344,] 0.0070011905
## [345,] 0.0111805976
## [346,] 0.0060645072
## [347,] 0.0081216967
## [348,] 0.0081299339
## [349,] 0.0438388192
## [350,] 0.0386646360
## [351,] 0.0082871256
## [352,] 0.0000000000
## [353,] 0.0037199341
## [354,] 0.0067248792
## [355,] 0.0573782705
## [356,] 0.0427848625
## [357,] 0.0035579569
## [358,] 0.0064947550
## [359,] 0.0046406126
## [360,] 0.1057534027
## [361,] 0.0057766701
## [362,] 0.0094070698
## [363,] 0.0795955956
## [364,] 0.0758246713
## [365,] 0.0596102396
## [366,] 0.0030555618
## [367,] 0.0186070923
## [368,] 0.0103203732
## [369,] 0.0746553991
## [370,] 0.0100937949
## [371,] 0.0393509834
## [372,] 0.0139557443
## [373,] 0.0032586082
## [374,] 0.0683234957
## [375,] 0.0040894915
## [376,] 0.0030258620
## [377,] 0.0089389107
## wavelet.HHH_glszm_GrayLevelNonUniformity
## [1,] 0.026717022
## [2,] 0.255653696
## [3,] 0.185367187
## [4,] 0.019507157
## [5,] 0.243163986
## [6,] 0.618837529
## [7,] 0.007447881
## [8,] 0.210237997
## [9,] 0.153401000
## [10,] 0.022282455
## [11,] 0.168074765
## [12,] 1.000000000
## [13,] 0.007447881
## [14,] 0.007447881
## [15,] 0.019110453
## [16,] 0.082394205
## [17,] 0.744968172
## [18,] 0.051252597
## [19,] 0.063183800
## [20,] 0.186307241
## [21,] 0.053799725
## [22,] 0.162354706
## [23,] 0.050664458
## [24,] 0.007447881
## [25,] 0.020264338
## [26,] 0.034205167
## [27,] 0.022282455
## [28,] 0.225613416
## [29,] 0.047525555
## [30,] 0.035564425
## [31,] 0.055365998
## [32,] 0.005587831
## [33,] 0.075221627
## [34,] 0.053172962
## [35,] 0.019815664
## [36,] 0.079786706
## [37,] 0.034359662
## [38,] 0.017840611
## [39,] 0.025978432
## [40,] 0.017346623
## [41,] 0.277961369
## [42,] 0.016729011
## [43,] 0.082394205
## [44,] 0.067253709
## [45,] 0.008935001
## [46,] 0.042915239
## [47,] 0.078286256
## [48,] 0.016729011
## [49,] 0.005587831
## [50,] 0.725897003
## [51,] 0.017840611
## [52,] 0.041236812
## [53,] 0.007447881
## [54,] 0.015934731
## [55,] 0.000000000
## [56,] 0.000000000
## [57,] 0.032127203
## [58,] 0.007447881
## [59,] 0.013948011
## [60,] 0.000000000
## [61,] 0.000000000
## [62,] 0.106263028
## [63,] 0.007447881
## [64,] 0.022282455
## [65,] 0.028615330
## [66,] 0.007447881
## [67,] 0.022282455
## [68,] 0.007447881
## [69,] 0.016729011
## [70,] 0.000000000
## [71,] 0.007447881
## [72,] 0.176996775
## [73,] 0.022282455
## [74,] 0.000000000
## [75,] 0.088952416
## [76,] 0.005587831
## [77,] 0.016729011
## [78,] 0.011164152
## [79,] 0.007447881
## [80,] 0.089323858
## [81,] 0.174238884
## [82,] 0.088050133
## [83,] 0.000000000
## [84,] 0.047525555
## [85,] 0.028615330
## [86,] 0.016729011
## [87,] 0.007447881
## [88,] 0.013948011
## [89,] 0.622945510
## [90,] 0.016729011
## [91,] 0.007447881
## [92,] 0.007447881
## [93,] 0.100151762
## [94,] 0.000000000
## [95,] 0.000000000
## [96,] 0.005587831
## [97,] 0.007447881
## [98,] 0.339423627
## [99,] 0.183132416
## [100,] 0.025978432
## [101,] 0.000000000
## [102,] 0.017840611
## [103,] 0.024131074
## [104,] 0.004471187
## [105,] 0.017840611
## [106,] 0.119675505
## [107,] 0.266881644
## [108,] 0.055365998
## [109,] 0.011164152
## [110,] 0.011164152
## [111,] 0.000000000
## [112,] 0.055842510
## [113,] 0.012755279
## [114,] 0.005587831
## [115,] 0.007447881
## [116,] 0.017840611
## [117,] 0.056583584
## [118,] 0.011164152
## [119,] 0.084766563
## [120,] 0.120689553
## [121,] 0.024131074
## [122,] 0.028615330
## [123,] 0.026717022
## [124,] 0.016729011
## [125,] 0.000000000
## [126,] 0.055365998
## [127,] 0.248449078
## [128,] 0.035564425
## [129,] 0.007447881
## [130,] 0.068729423
## [131,] 0.000000000
## [132,] 0.000000000
## [133,] 0.044383009
## [134,] 0.000000000
## [135,] 0.007447881
## [136,] 0.113777874
## [137,] 0.032350532
## [138,] 0.005587831
## [139,] 0.091914948
## [140,] 0.007447881
## [141,] 0.038874761
## [142,] 0.007447881
## [143,] 0.000000000
## [144,] 0.011164152
## [145,] 0.004471187
## [146,] 0.007447881
## [147,] 0.119155148
## [148,] 0.438999411
## [149,] 0.007447881
## [150,] 0.007447881
## [151,] 0.000000000
## [152,] 0.136661052
## [153,] 0.004471187
## [154,] 0.191427612
## [155,] 0.007447881
## [156,] 0.195746636
## [157,] 0.028615330
## [158,] 0.046583174
## [159,] 0.000000000
## [160,] 0.091142426
## [161,] 0.009572089
## [162,] 0.000000000
## [163,] 0.101552918
## [164,] 0.037901553
## [165,] 0.688265999
## [166,] 0.000000000
## [167,] 0.065091389
## [168,] 0.007447881
## [169,] 0.371296735
## [170,] 0.188414496
## [171,] 0.053172962
## [172,] 0.059473159
## [173,] 0.035904133
## [174,] 0.022282455
## [175,] 0.017840611
## [176,] 0.007447881
## [177,] 0.026717022
## [178,] 0.129247069
## [179,] 0.062855084
## [180,] 0.025978432
## [181,] 0.063574098
## [182,] 0.016729011
## [183,] 0.148409620
## [184,] 0.004471187
## [185,] 0.007447881
## [186,] 0.029101861
## [187,] 0.101552918
## [188,] 0.005587831
## [189,] 0.000000000
## [190,] 0.174790369
## [191,] 0.044383009
## [192,] 0.022282455
## [193,] 0.026314180
## [194,] 0.222518888
## [195,] 0.097563106
## [196,] 0.009572089
## [197,] 0.203795263
## [198,] 0.095835959
## [199,] 0.011164152
## [200,] 0.053799725
## [201,] 0.205777504
## [202,] 0.159626107
## [203,] 0.124747018
## [204,] 0.000000000
## [205,] 0.104604210
## [206,] 0.075990857
## [207,] 0.000000000
## [208,] 0.017840611
## [209,] 0.017346623
## [210,] 0.026717022
## [211,] 0.014875328
## [212,] 0.036116429
## [213,] 0.004471187
## [214,] 0.019815664
## [215,] 0.034933426
## [216,] 0.000000000
## [217,] 0.056931365
## [218,] 0.007447881
## [219,] 0.177820016
## [220,] 0.007447881
## [221,] 0.000000000
## [222,] 0.007447881
## [223,] 0.016729011
## [224,] 0.007447881
## [225,] 0.070395223
## [226,] 0.036116429
## [227,] 0.029101861
## [228,] 0.007447881
## [229,] 0.000000000
## [230,] 0.075645515
## [231,] 0.044383009
## [232,] 0.025978432
## [233,] 0.034933426
## [234,] 0.007447881
## [235,] 0.034933426
## [236,] 0.017840611
## [237,] 0.007447881
## [238,] 0.007447881
## [239,] 0.007447881
## [240,] 0.000000000
## [241,] 0.076796482
## [242,] 0.025978432
## [243,] 0.007447881
## [244,] 0.072120516
## [245,] 0.011164152
## [246,] 0.007447881
## [247,] 0.022282455
## [248,] 0.022282455
## [249,] 0.007447881
## [250,] 0.098223195
## [251,] 0.017840611
## [252,] 0.007447881
## [253,] 0.014875328
## [254,] 0.026717022
## [255,] 0.050379253
## [256,] 0.047525555
## [257,] 0.005587831
## [258,] 0.008935001
## [259,] 0.007447881
## [260,] 0.028615330
## [261,] 0.016729011
## [262,] 0.007447881
## [263,] 0.033355284
## [264,] 0.028615330
## [265,] 0.059473159
## [266,] 0.007447881
## [267,] 0.052075777
## [268,] 0.022282455
## [269,] 0.121211881
## [270,] 0.039487343
## [271,] 0.007447881
## [272,] 0.110645357
## [273,] 0.090596984
## [274,] 0.007447881
## [275,] 0.138626750
## [276,] 0.000000000
## [277,] 0.007447881
## [278,] 0.007447881
## [279,] 0.016729011
## [280,] 0.012755279
## [281,] 0.083047415
## [282,] 0.132877145
## [283,] 0.011164152
## [284,] 0.037036186
## [285,] 0.025978432
## [286,] 0.012755279
## [287,] 0.000000000
## [288,] 0.017840611
## [289,] 0.064624621
## [290,] 0.011164152
## [291,] 0.025978432
## [292,] 0.022282455
## [293,] 0.016729011
## [294,] 0.026717022
## [295,] 0.007447881
## [296,] 0.022282455
## [297,] 0.025978432
## [298,] 0.000000000
## [299,] 0.026717022
## [300,] 0.026717022
## [301,] 0.028615330
## [302,] 0.033355284
## [303,] 0.067863668
## [304,] 0.014875328
## [305,] 0.000000000
## [306,] 0.007447881
## [307,] 0.093743727
## [308,] 0.044383009
## [309,] 0.000000000
## [310,] 0.057192172
## [311,] 0.051147043
## [312,] 0.007447881
## [313,] 0.007447881
## [314,] 0.026717022
## [315,] 0.054369383
## [316,] 0.050379253
## [317,] 0.017840611
## [318,] 0.026717022
## [319,] 0.057192172
## [320,] 0.016729011
## [321,] 0.044383009
## [322,] 0.028615330
## [323,] 0.017840611
## [324,] 0.017840611
## [325,] 0.072120516
## [326,] 0.072120516
## [327,] 0.000000000
## [328,] 0.017840611
## [329,] 0.000000000
## [330,] 0.072120516
## [331,] 0.037036186
## [332,] 0.025978432
## [333,] 0.064624621
## [334,] 0.028615330
## [335,] 0.071757388
## [336,] 0.017840611
## [337,] 0.017840611
## [338,] 0.000000000
## [339,] 0.025978432
## [340,] 0.016729011
## [341,] 0.007447881
## [342,] 0.033355284
## [343,] 0.000000000
## [344,] 0.007447881
## [345,] 0.045483315
## [346,] 0.011164152
## [347,] 0.026717022
## [348,] 0.071757388
## [349,] 0.011164152
## [350,] 0.017840611
## [351,] 0.033355284
## [352,] 0.011164152
## [353,] 0.092858842
## [354,] 0.025978432
## [355,] 0.030196237
## [356,] 0.028615330
## [357,] 0.064624621
## [358,] 0.053172962
## [359,] 0.017840611
## [360,] 0.024500646
## [361,] 0.022282455
## [362,] 0.034933426
## [363,] 0.053431058
## [364,] 0.019110453
## [365,] 0.053538591
## [366,] 0.046583174
## [367,] 0.007447881
## [368,] 0.025978432
## [369,] 0.013948011
## [370,] 0.011164152
## [371,] 0.000000000
## [372,] 0.017840611
## [373,] 0.033355284
## [374,] 0.110517506
## [375,] 0.044383009
## [376,] 0.016729011
## [377,] 0.039487343
## wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis
## [1,] 0.356553034
## [2,] 0.572481924
## [3,] 0.269312366
## [4,] 0.231983425
## [5,] 0.731475913
## [6,] 0.486811395
## [7,] 0.234049529
## [8,] 0.312139633
## [9,] 0.276308674
## [10,] 0.304277203
## [11,] 0.239927201
## [12,] 0.387771011
## [13,] 0.132140321
## [14,] 0.318963310
## [15,] 0.258439599
## [16,] 0.247331615
## [17,] 0.272592662
## [18,] 0.526417702
## [19,] 0.357822107
## [20,] 0.490763461
## [21,] 0.208138148
## [22,] 0.439136747
## [23,] 0.434738338
## [24,] 0.332034388
## [25,] 0.668550622
## [26,] 0.294260223
## [27,] 0.472953007
## [28,] 0.892559764
## [29,] 0.432726545
## [30,] 0.322308457
## [31,] 0.434899375
## [32,] 0.250453871
## [33,] 0.463058181
## [34,] 0.416441489
## [35,] 0.343801315
## [36,] 0.505485718
## [37,] 0.503013259
## [38,] 0.415752679
## [39,] 0.531904954
## [40,] 0.250238220
## [41,] 0.455109909
## [42,] 0.184641301
## [43,] 0.163204097
## [44,] 0.412597323
## [45,] 0.273197013
## [46,] 0.772392025
## [47,] 0.378580673
## [48,] 0.257731918
## [49,] 0.190901950
## [50,] 0.366134150
## [51,] 0.309435234
## [52,] 0.308265936
## [53,] 0.140394098
## [54,] 0.209924249
## [55,] 0.271812785
## [56,] 0.293267048
## [57,] 0.478097048
## [58,] 0.213672568
## [59,] 0.240510972
## [60,] 0.346965039
## [61,] 0.270507227
## [62,] 0.414029472
## [63,] 0.220846169
## [64,] 0.321676997
## [65,] 0.409498731
## [66,] 0.136772473
## [67,] 0.267718842
## [68,] 0.175873152
## [69,] 0.240200399
## [70,] 0.231910000
## [71,] 0.189409492
## [72,] 0.354143612
## [73,] 0.243578922
## [74,] 0.097324064
## [75,] 0.319739327
## [76,] 0.243638209
## [77,] 0.635910486
## [78,] 0.343188236
## [79,] 0.370574474
## [80,] 1.000000000
## [81,] 0.405443298
## [82,] 0.286871541
## [83,] 0.118332624
## [84,] 0.346050450
## [85,] 0.312491049
## [86,] 0.159552370
## [87,] 0.206190486
## [88,] 0.404188886
## [89,] 0.573990418
## [90,] 0.168944852
## [91,] 0.461986571
## [92,] 0.188181714
## [93,] 0.530482269
## [94,] 0.140447191
## [95,] 0.322034490
## [96,] 0.267540770
## [97,] 0.127545710
## [98,] 0.466504006
## [99,] 0.447289030
## [100,] 0.183589917
## [101,] 0.165574095
## [102,] 0.150211055
## [103,] 0.244177958
## [104,] 0.316756360
## [105,] 0.220602116
## [106,] 0.314119281
## [107,] 0.489491473
## [108,] 0.342203198
## [109,] 0.206287485
## [110,] 0.243960016
## [111,] 0.393526285
## [112,] 0.465423964
## [113,] 0.298488439
## [114,] 0.312687480
## [115,] 0.096261085
## [116,] 0.209941547
## [117,] 0.471743570
## [118,] 0.219029586
## [119,] 0.463899945
## [120,] 0.592522092
## [121,] 0.284421475
## [122,] 0.188401502
## [123,] 0.202946049
## [124,] 0.202391299
## [125,] 0.156388974
## [126,] 0.342412035
## [127,] 0.456947896
## [128,] 0.394019356
## [129,] 0.135529530
## [130,] 0.435793554
## [131,] 0.201496988
## [132,] 0.244404135
## [133,] 0.578948749
## [134,] 0.000000000
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## [136,] 0.680853793
## [137,] 0.321864891
## [138,] 0.435643421
## [139,] 0.455499995
## [140,] 0.178322672
## [141,] 0.330520004
## [142,] 0.401341752
## [143,] 0.232552125
## [144,] 0.222278947
## [145,] 0.318786406
## [146,] 0.177134530
## [147,] 0.498345998
## [148,] 0.566601048
## [149,] 0.119882659
## [150,] 0.254182417
## [151,] 0.144772317
## [152,] 0.476725172
## [153,] 0.221017182
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## [155,] 0.171070110
## [156,] 0.725200258
## [157,] 0.215211969
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## [159,] 0.285494681
## [160,] 0.294989873
## [161,] 0.403780489
## [162,] 0.134117271
## [163,] 0.315227817
## [164,] 0.266473118
## [165,] 0.533528999
## [166,] 0.157528361
## [167,] 0.508505584
## [168,] 0.255571905
## [169,] 0.480361689
## [170,] 0.415202056
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## [172,] 0.498674469
## [173,] 0.484384149
## [174,] 0.341814786
## [175,] 0.117734408
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## [177,] 0.233740145
## [178,] 0.500503584
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## [180,] 0.356644859
## [181,] 0.255005570
## [182,] 0.155748719
## [183,] 0.719029337
## [184,] 0.276772648
## [185,] 0.258101120
## [186,] 0.204935195
## [187,] 0.396129039
## [188,] 0.076721000
## [189,] 0.197824635
## [190,] 0.437363252
## [191,] 0.304567551
## [192,] 0.263967830
## [193,] 0.322455393
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## [195,] 0.345295714
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## [200,] 0.287074678
## [201,] 0.353133456
## [202,] 0.214728553
## [203,] 0.921305109
## [204,] 0.032717412
## [205,] 0.448519089
## [206,] 0.342920117
## [207,] 0.128529084
## [208,] 0.334284141
## [209,] 0.188208163
## [210,] 0.309384489
## [211,] 0.272190739
## [212,] 0.270145428
## [213,] 0.223010903
## [214,] 0.260305180
## [215,] 0.222069229
## [216,] 0.157815884
## [217,] 0.453833959
## [218,] 0.196220434
## [219,] 0.400722280
## [220,] 0.066462397
## [221,] 0.234793112
## [222,] 0.151702931
## [223,] 0.189831599
## [224,] 0.179932075
## [225,] 0.650256454
## [226,] 0.373754047
## [227,] 0.443757659
## [228,] 0.359928850
## [229,] 0.305353858
## [230,] 0.342443152
## [231,] 0.292495030
## [232,] 0.376563840
## [233,] 0.257705656
## [234,] 0.305895825
## [235,] 0.170489429
## [236,] 0.324073558
## [237,] 0.165772605
## [238,] 0.062067646
## [239,] 0.308230340
## [240,] 0.188176530
## [241,] 0.546478355
## [242,] 0.229505847
## [243,] 0.142536940
## [244,] 0.335242111
## [245,] 0.136507018
## [246,] 0.311672292
## [247,] 0.229940318
## [248,] 0.310915929
## [249,] 0.068966049
## [250,] 0.388207233
## [251,] 0.274934705
## [252,] 0.148868128
## [253,] 0.223215315
## [254,] 0.042499006
## [255,] 0.388973323
## [256,] 0.475819768
## [257,] 0.633367350
## [258,] 0.318696890
## [259,] 0.309246424
## [260,] 0.280811194
## [261,] 0.098328381
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## [263,] 0.233740748
## [264,] 0.295951326
## [265,] 0.469537380
## [266,] 0.302997805
## [267,] 0.415616686
## [268,] 0.546378899
## [269,] 0.705270777
## [270,] 0.276591038
## [271,] 0.111322254
## [272,] 0.537232471
## [273,] 0.497888621
## [274,] 0.050232419
## [275,] 0.699429627
## [276,] 0.129705143
## [277,] 0.106918952
## [278,] 0.361548836
## [279,] 0.160530654
## [280,] 0.117534867
## [281,] 0.586091141
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## [283,] 0.236213808
## [284,] 0.344159232
## [285,] 0.253894449
## [286,] 0.553506466
## [287,] 0.162678898
## [288,] 0.107648284
## [289,] 0.548513857
## [290,] 0.281059313
## [291,] 0.298986931
## [292,] 0.267075652
## [293,] 0.062936151
## [294,] 0.099881689
## [295,] 0.284137735
## [296,] 0.318818519
## [297,] 0.387643236
## [298,] 0.130044376
## [299,] 0.294469240
## [300,] 0.146894897
## [301,] 0.171399592
## [302,] 0.782479290
## [303,] 0.244969726
## [304,] 0.276231142
## [305,] 0.085978502
## [306,] 0.117247107
## [307,] 0.498873198
## [308,] 0.211889216
## [309,] 0.155393823
## [310,] 0.360648344
## [311,] 0.423277423
## [312,] 0.285118469
## [313,] 0.237835876
## [314,] 0.239951337
## [315,] 0.310086839
## [316,] 0.363803716
## [317,] 0.151406640
## [318,] 0.309579150
## [319,] 0.348213205
## [320,] 0.188822782
## [321,] 0.291523678
## [322,] 0.611947501
## [323,] 0.568113051
## [324,] 0.119499479
## [325,] 0.253553413
## [326,] 0.460579845
## [327,] 0.108163912
## [328,] 0.184808183
## [329,] 0.195369961
## [330,] 0.256433388
## [331,] 0.278548075
## [332,] 0.235445897
## [333,] 0.458280157
## [334,] 0.350843551
## [335,] 0.453202159
## [336,] 0.381196145
## [337,] 0.093730620
## [338,] 0.293667884
## [339,] 0.339431603
## [340,] 0.387364834
## [341,] 0.170540270
## [342,] 0.327408408
## [343,] 0.004173511
## [344,] 0.045798647
## [345,] 0.450412100
## [346,] 0.043731043
## [347,] 0.225587525
## [348,] 0.244268782
## [349,] 0.229671868
## [350,] 0.131666523
## [351,] 0.296799710
## [352,] 0.109877178
## [353,] 0.569022022
## [354,] 0.223701785
## [355,] 0.227244731
## [356,] 0.144391929
## [357,] 0.402088916
## [358,] 0.324759216
## [359,] 0.399606944
## [360,] 0.731067930
## [361,] 0.347206678
## [362,] 0.215582848
## [363,] 0.203123387
## [364,] 0.496509835
## [365,] 0.394747274
## [366,] 0.317453155
## [367,] 0.276950900
## [368,] 0.541762451
## [369,] 0.230780829
## [370,] 0.248773222
## [371,] 0.150531762
## [372,] 0.523091674
## [373,] 0.369807538
## [374,] 0.792060962
## [375,] 0.342516306
## [376,] 0.171463155
## [377,] 0.343255918
## wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis
## [1,] 0.0208979179
## [2,] 0.0099012744
## [3,] 0.0667798090
## [4,] 0.0154040603
## [5,] 0.0005540424
## [6,] 0.0240115697
## [7,] 0.0749325934
## [8,] 0.0495552441
## [9,] 0.0152182925
## [10,] 0.0188157316
## [11,] 0.0346313244
## [12,] 0.0175560510
## [13,] 0.0399279401
## [14,] 0.0610754084
## [15,] 0.1212963308
## [16,] 0.0139166084
## [17,] 0.0161440316
## [18,] 0.0037167472
## [19,] 0.0237878804
## [20,] 0.0185313328
## [21,] 0.0952093108
## [22,] 0.0229081057
## [23,] 0.0105795826
## [24,] 0.0229289318
## [25,] 0.0030881307
## [26,] 0.0198180679
## [27,] 0.0039636199
## [28,] 0.0003354789
## [29,] 0.0146887679
## [30,] 0.0248005487
## [31,] 0.0118561488
## [32,] 0.0613207194
## [33,] 0.0075904387
## [34,] 0.0300458518
## [35,] 0.0407820841
## [36,] 0.0000000000
## [37,] 0.0024665612
## [38,] 0.0568981331
## [39,] 0.0134777134
## [40,] 0.0459419768
## [41,] 0.0343333651
## [42,] 0.1401550207
## [43,] 0.0846308789
## [44,] 0.0152493421
## [45,] 0.0304720997
## [46,] 0.0035248679
## [47,] 0.0240285726
## [48,] 0.0283807485
## [49,] 0.0417283940
## [50,] 0.0324999740
## [51,] 0.0134514784
## [52,] 0.0191240655
## [53,] 0.0910298368
## [54,] 0.0928833509
## [55,] 0.0865145441
## [56,] 0.0560493331
## [57,] 0.0046389822
## [58,] 0.0800437341
## [59,] 0.0344402650
## [60,] 0.0404657646
## [61,] 0.0616475497
## [62,] 0.0198990223
## [63,] 0.0199321985
## [64,] 0.0163033660
## [65,] 0.0094893272
## [66,] 0.0552132442
## [67,] 0.0306077791
## [68,] 0.1617405453
## [69,] 0.0329395251
## [70,] 0.2112591514
## [71,] 0.0614587420
## [72,] 0.0853674636
## [73,] 0.0524475498
## [74,] 0.3765432913
## [75,] 0.0325341967
## [76,] 0.2488844118
## [77,] 0.0046875502
## [78,] 0.0359888701
## [79,] 0.0225509301
## [80,] 0.0023500786
## [81,] 0.0104646794
## [82,] 0.0123804508
## [83,] 0.2771526512
## [84,] 0.0270287896
## [85,] 0.0197241220
## [86,] 0.1142805517
## [87,] 0.1130621527
## [88,] 0.0154353435
## [89,] 0.0093297900
## [90,] 0.1663169267
## [91,] 0.0227268129
## [92,] 0.0750098419
## [93,] 0.0157086121
## [94,] 0.4312904695
## [95,] 0.0350905671
## [96,] 0.0171309501
## [97,] 0.1596323040
## [98,] 0.0099573685
## [99,] 0.0055227359
## [100,] 0.0212344484
## [101,] 0.1009480625
## [102,] 0.1566288080
## [103,] 0.0173860280
## [104,] 0.0160031409
## [105,] 0.1727931443
## [106,] 0.0249234571
## [107,] 0.0356440061
## [108,] 0.0255498365
## [109,] 0.0653549060
## [110,] 0.0412245723
## [111,] 0.0241500427
## [112,] 0.0068032338
## [113,] 0.0925288827
## [114,] 0.0093433434
## [115,] 0.1929852684
## [116,] 0.1381762244
## [117,] 0.0058890580
## [118,] 0.1326463077
## [119,] 0.0015497254
## [120,] 0.0105364859
## [121,] 0.0293701418
## [122,] 0.0212231677
## [123,] 0.0245391043
## [124,] 0.0278080773
## [125,] 0.0365110783
## [126,] 0.0252884396
## [127,] 0.0090483589
## [128,] 0.0072827451
## [129,] 0.0531420688
## [130,] 0.0166306155
## [131,] 0.1167563448
## [132,] 0.1222937631
## [133,] 0.0174124476
## [134,] 0.6225046976
## [135,] 0.2405805466
## [136,] 0.0073176548
## [137,] 0.1175745372
## [138,] 0.0541771001
## [139,] 0.0119473721
## [140,] 0.0543783874
## [141,] 0.0895986608
## [142,] 0.0345664185
## [143,] 0.3648384822
## [144,] 0.0261707720
## [145,] 0.0489167138
## [146,] 0.0842735907
## [147,] 0.0134129081
## [148,] 0.0061879384
## [149,] 0.0516754316
## [150,] 0.0404991988
## [151,] 0.1088281384
## [152,] 0.0084424424
## [153,] 0.1266441897
## [154,] 0.0051165202
## [155,] 0.0495153946
## [156,] 0.0027012565
## [157,] 0.1496330289
## [158,] 0.0290172645
## [159,] 0.0356892166
## [160,] 0.0257924513
## [161,] 0.0339690955
## [162,] 0.1747665052
## [163,] 0.0246810236
## [164,] 0.0064156546
## [165,] 0.0189841503
## [166,] 0.2021816991
## [167,] 0.0222035165
## [168,] 0.1217546554
## [169,] 0.0276149591
## [170,] 0.0231080842
## [171,] 0.0344532255
## [172,] 0.0120953907
## [173,] 0.0133252370
## [174,] 0.0170796947
## [175,] 0.0484366967
## [176,] 0.0526333665
## [177,] 0.0441301555
## [178,] 0.0111953460
## [179,] 0.0885808676
## [180,] 0.0342967410
## [181,] 0.0289798692
## [182,] 0.0475495557
## [183,] 0.0044665304
## [184,] 0.0530926145
## [185,] 0.1422166868
## [186,] 0.0416436346
## [187,] 0.0215758546
## [188,] 0.1382185479
## [189,] 0.1958937913
## [190,] 0.0125577142
## [191,] 0.0256108824
## [192,] 0.0170494963
## [193,] 0.0359047500
## [194,] 0.0162580974
## [195,] 0.0192833389
## [196,] 0.0618586985
## [197,] 0.0127744407
## [198,] 0.0226604493
## [199,] 0.0403386661
## [200,] 0.0203771504
## [201,] 0.0363247335
## [202,] 0.0300136997
## [203,] 0.0020874095
## [204,] 0.1306256305
## [205,] 0.0209783526
## [206,] 0.0347335481
## [207,] 0.0913861413
## [208,] 0.0315021348
## [209,] 0.0114200742
## [210,] 0.0226365331
## [211,] 0.0243754048
## [212,] 0.0113059374
## [213,] 0.1217568506
## [214,] 0.0174361231
## [215,] 0.0408780712
## [216,] 0.0666183848
## [217,] 0.0049429533
## [218,] 0.0870440148
## [219,] 0.0186521655
## [220,] 0.2697610902
## [221,] 0.0611987874
## [222,] 0.0752898668
## [223,] 0.1253274126
## [224,] 0.1406745328
## [225,] 0.0250553374
## [226,] 0.0165282553
## [227,] 0.0330394072
## [228,] 0.0132290793
## [229,] 0.0537888948
## [230,] 0.0071550173
## [231,] 0.0263335229
## [232,] 0.0587831075
## [233,] 0.0912606276
## [234,] 0.1041496743
## [235,] 0.0604604748
## [236,] 0.0365825254
## [237,] 0.0944594897
## [238,] 0.2091687314
## [239,] 0.0546306844
## [240,] 0.0544577531
## [241,] 0.0108354202
## [242,] 0.0399647771
## [243,] 0.0522149546
## [244,] 0.0069185630
## [245,] 0.0151858557
## [246,] 0.0202165197
## [247,] 0.0122211785
## [248,] 0.1518262276
## [249,] 1.0000000000
## [250,] 0.0047384665
## [251,] 0.0488513427
## [252,] 0.0595963656
## [253,] 0.0371344609
## [254,] 0.7218147805
## [255,] 0.0177577411
## [256,] 0.0117613167
## [257,] 0.0149849793
## [258,] 0.0175423676
## [259,] 0.0390615756
## [260,] 0.0359870870
## [261,] 0.1418109100
## [262,] 0.1328347025
## [263,] 0.0414616788
## [264,] 0.0320740752
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## [266,] 0.0152572153
## [267,] 0.0085399666
## [268,] 0.0173540550
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## [270,] 0.0375162028
## [271,] 0.1736929304
## [272,] 0.0050395329
## [273,] 0.0234510719
## [274,] 0.3559880471
## [275,] 0.0041121143
## [276,] 0.0592783290
## [277,] 0.0463887155
## [278,] 0.0043899588
## [279,] 0.0622994583
## [280,] 0.0566966398
## [281,] 0.0210106020
## [282,] 0.0139587606
## [283,] 0.1628768465
## [284,] 0.0659688378
## [285,] 0.0238197584
## [286,] 0.0142615361
## [287,] 0.2353923963
## [288,] 0.2777986994
## [289,] 0.0055356850
## [290,] 0.1458963788
## [291,] 0.0529660625
## [292,] 0.0163512374
## [293,] 0.1480102657
## [294,] 0.0591992149
## [295,] 0.0069164970
## [296,] 0.0203891549
## [297,] 0.0267910679
## [298,] 0.0481611364
## [299,] 0.0126599105
## [300,] 0.0876688255
## [301,] 0.0634276951
## [302,] 0.0008132195
## [303,] 0.0143700137
## [304,] 0.0345866255
## [305,] 0.1017822525
## [306,] 0.2218525923
## [307,] 0.0175244591
## [308,] 0.0311453541
## [309,] 0.0562129293
## [310,] 0.0388223981
## [311,] 0.0183130671
## [312,] 0.0544732812
## [313,] 0.0438331316
## [314,] 0.0645772502
## [315,] 0.0146099643
## [316,] 0.0112878757
## [317,] 0.1461791457
## [318,] 0.0215931905
## [319,] 0.0058946577
## [320,] 0.1141856138
## [321,] 0.0642952062
## [322,] 0.0307932806
## [323,] 0.0561318375
## [324,] 0.1689280466
## [325,] 0.0262489128
## [326,] 0.0216861949
## [327,] 0.1034334538
## [328,] 0.0221767138
## [329,] 0.0627829388
## [330,] 0.0928424534
## [331,] 0.0433516542
## [332,] 0.0542225098
## [333,] 0.0145513234
## [334,] 0.0108591835
## [335,] 0.0016903146
## [336,] 0.0672545891
## [337,] 0.0986664995
## [338,] 0.0341548449
## [339,] 0.0187173319
## [340,] 0.0268870391
## [341,] 0.1010368290
## [342,] 0.0557826180
## [343,] 0.3100776788
## [344,] 0.4589549646
## [345,] 0.0018865697
## [346,] 0.1223133341
## [347,] 0.1220021566
## [348,] 0.0376990645
## [349,] 0.0770845175
## [350,] 0.1172782663
## [351,] 0.0130933265
## [352,] 0.1603284615
## [353,] 0.0077006452
## [354,] 0.0291118509
## [355,] 0.0425638417
## [356,] 0.2117067556
## [357,] 0.0058839640
## [358,] 0.0298215175
## [359,] 0.0798530072
## [360,] 0.0206261449
## [361,] 0.0375758293
## [362,] 0.0588363477
## [363,] 0.0026829758
## [364,] 0.0271433719
## [365,] 0.0110391829
## [366,] 0.0580388089
## [367,] 0.1396900695
## [368,] 0.0148364334
## [369,] 0.0707643898
## [370,] 0.1204070197
## [371,] 0.2304568105
## [372,] 0.0191907631
## [373,] 0.0086162500
## [374,] 0.0003027023
## [375,] 0.0185586674
## [376,] 0.0595968755
## [377,] 0.0218431978
## wavelet.HHH_glcm_ClusterProminence
## [1,] 0.092617414
## [2,] 0.026323911
## [3,] 0.086955325
## [4,] 0.032102560
## [5,] 0.155405366
## [6,] 0.145304666
## [7,] 0.127428114
## [8,] 0.072604495
## [9,] 0.035547955
## [10,] 0.058018052
## [11,] 0.043756979
## [12,] 0.078326836
## [13,] 0.012876747
## [14,] 0.066425013
## [15,] 0.099203364
## [16,] 0.023295589
## [17,] 0.059343238
## [18,] 0.037675814
## [19,] 0.010914193
## [20,] 0.084004048
## [21,] 0.055009044
## [22,] 0.068813928
## [23,] 0.026518659
## [24,] 0.095944924
## [25,] 0.053884995
## [26,] 0.053332946
## [27,] 0.078985905
## [28,] 0.066992211
## [29,] 0.035892406
## [30,] 0.037949484
## [31,] 0.038687387
## [32,] 0.078541920
## [33,] 0.043026867
## [34,] 0.008113150
## [35,] 0.022455924
## [36,] 0.031900574
## [37,] 0.038603631
## [38,] 0.052025327
## [39,] 0.039164943
## [40,] 0.034908805
## [41,] 0.103197072
## [42,] 0.177908509
## [43,] 0.326746285
## [44,] 0.039340550
## [45,] 0.046522233
## [46,] 0.016492078
## [47,] 0.065973917
## [48,] 0.020188951
## [49,] 0.011900970
## [50,] 0.141049150
## [51,] 0.048295912
## [52,] 0.006460564
## [53,] 0.079514265
## [54,] 0.084397206
## [55,] 0.030663122
## [56,] 0.063502627
## [57,] 0.006776462
## [58,] 0.028033096
## [59,] 0.067279616
## [60,] 0.034215221
## [61,] 0.032470197
## [62,] 0.037858131
## [63,] 0.030535401
## [64,] 0.024351845
## [65,] 0.022090703
## [66,] 0.093186708
## [67,] 0.045051945
## [68,] 0.046746124
## [69,] 0.019403401
## [70,] 0.047742199
## [71,] 0.058193311
## [72,] 0.242497380
## [73,] 0.060623873
## [74,] 0.061433025
## [75,] 0.125416178
## [76,] 0.104885196
## [77,] 0.012475665
## [78,] 0.031793045
## [79,] 0.027086480
## [80,] 0.011128400
## [81,] 0.063403153
## [82,] 0.009900188
## [83,] 0.097640361
## [84,] 0.038891246
## [85,] 0.030294822
## [86,] 0.064690658
## [87,] 0.084473792
## [88,] 0.047420528
## [89,] 0.096390879
## [90,] 0.049666276
## [91,] 0.052093747
## [92,] 0.110001221
## [93,] 0.033036392
## [94,] 0.097247758
## [95,] 0.020338581
## [96,] 0.034884724
## [97,] 0.079736036
## [98,] 0.043598126
## [99,] 0.022292125
## [100,] 0.000000000
## [101,] 0.077005779
## [102,] 0.052070500
## [103,] 0.032784277
## [104,] 0.043051015
## [105,] 0.049764688
## [106,] 0.057815100
## [107,] 0.211958921
## [108,] 0.063097846
## [109,] 0.059448426
## [110,] 0.086223930
## [111,] 0.007357334
## [112,] 0.013370014
## [113,] 0.074954124
## [114,] 0.026751272
## [115,] 0.075331099
## [116,] 0.097094598
## [117,] 0.040462259
## [118,] 0.049554112
## [119,] 0.022614716
## [120,] 0.022138977
## [121,] 0.021449026
## [122,] 0.015787563
## [123,] 0.004165803
## [124,] 0.022752277
## [125,] 0.072409335
## [126,] 0.046976689
## [127,] 0.054225277
## [128,] 0.005284757
## [129,] 0.060307633
## [130,] 0.073555391
## [131,] 0.091743304
## [132,] 0.105878966
## [133,] 0.057086067
## [134,] 0.042079988
## [135,] 0.094210170
## [136,] 0.093627751
## [137,] 0.180475648
## [138,] 0.062722765
## [139,] 0.026351418
## [140,] 0.047166964
## [141,] 0.102898397
## [142,] 0.029596229
## [143,] 0.148440420
## [144,] 0.040952287
## [145,] 0.102351919
## [146,] 0.012532924
## [147,] 0.021594679
## [148,] 0.064411856
## [149,] 0.144691986
## [150,] 0.052805003
## [151,] 0.068900219
## [152,] 0.065218290
## [153,] 0.121458030
## [154,] 0.027660996
## [155,] 0.029664628
## [156,] 0.039886428
## [157,] 0.008143541
## [158,] 0.047674104
## [159,] 0.033238810
## [160,] 0.019029080
## [161,] 0.040048306
## [162,] 0.070675410
## [163,] 0.039570624
## [164,] 0.015834739
## [165,] 0.136426680
## [166,] 0.064978751
## [167,] 0.029704742
## [168,] 0.094821035
## [169,] 0.023229185
## [170,] 0.128681291
## [171,] 0.036930540
## [172,] 0.050636941
## [173,] 0.020172442
## [174,] 0.015001413
## [175,] 0.102284875
## [176,] 0.056544879
## [177,] 0.083326443
## [178,] 0.061912792
## [179,] 0.117222233
## [180,] 0.036601545
## [181,] 0.087321232
## [182,] 0.034985830
## [183,] 0.052754002
## [184,] 0.023888308
## [185,] 0.036376259
## [186,] 0.061502661
## [187,] 0.042070324
## [188,] 0.052606467
## [189,] 0.031682022
## [190,] 0.019773985
## [191,] 0.011147586
## [192,] 0.015788124
## [193,] 0.019280862
## [194,] 0.024193966
## [195,] 0.150516559
## [196,] 0.059773709
## [197,] 0.051243706
## [198,] 0.022431425
## [199,] 0.077998499
## [200,] 0.054015950
## [201,] 0.045558163
## [202,] 0.014528522
## [203,] 1.000000000
## [204,] 0.141898596
## [205,] 0.035013583
## [206,] 0.002641160
## [207,] 0.065369778
## [208,] 0.078168605
## [209,] 0.071912178
## [210,] 0.091836094
## [211,] 0.073663679
## [212,] 0.057112846
## [213,] 0.059972915
## [214,] 0.018285548
## [215,] 0.100156868
## [216,] 0.047481002
## [217,] 0.074580093
## [218,] 0.125106002
## [219,] 0.054820065
## [220,] 0.098277922
## [221,] 0.132449850
## [222,] 0.092614829
## [223,] 0.065825871
## [224,] 0.112573829
## [225,] 0.076475846
## [226,] 0.062951544
## [227,] 0.023364186
## [228,] 0.070350919
## [229,] 0.036592902
## [230,] 0.073240687
## [231,] 0.048075591
## [232,] 0.075151295
## [233,] 0.106294994
## [234,] 0.128185831
## [235,] 0.051489287
## [236,] 0.083629497
## [237,] 0.079289196
## [238,] 0.167307017
## [239,] 0.092211830
## [240,] 0.045671936
## [241,] 0.113573089
## [242,] 0.090361345
## [243,] 0.065133134
## [244,] 0.055500249
## [245,] 0.095901389
## [246,] 0.083357336
## [247,] 0.071712006
## [248,] 0.198466641
## [249,] 0.134036644
## [250,] 0.063355254
## [251,] 0.127038430
## [252,] 0.025812393
## [253,] 0.032142032
## [254,] 0.151241310
## [255,] 0.054930212
## [256,] 0.063573710
## [257,] 0.017405237
## [258,] 0.023757629
## [259,] 0.153575743
## [260,] 0.110897585
## [261,] 0.107516052
## [262,] 0.094184574
## [263,] 0.106989246
## [264,] 0.128815542
## [265,] 0.034334742
## [266,] 0.104461272
## [267,] 0.019301019
## [268,] 0.067074331
## [269,] 0.112849483
## [270,] 0.062379063
## [271,] 0.143092328
## [272,] 0.060362097
## [273,] 0.057229421
## [274,] 0.155039280
## [275,] 0.150738869
## [276,] 0.102506342
## [277,] 0.049580341
## [278,] 0.074183417
## [279,] 0.086616537
## [280,] 0.150704109
## [281,] 0.066772307
## [282,] 0.099885423
## [283,] 0.136125395
## [284,] 0.109864638
## [285,] 0.061614848
## [286,] 0.090050833
## [287,] 0.138804228
## [288,] 0.110445259
## [289,] 0.073525335
## [290,] 0.153307077
## [291,] 0.081593661
## [292,] 0.050090975
## [293,] 0.054951293
## [294,] 0.101814763
## [295,] 0.059587121
## [296,] 0.076634139
## [297,] 0.096174985
## [298,] 0.057517896
## [299,] 0.074123231
## [300,] 0.101979681
## [301,] 0.076360558
## [302,] 0.077038040
## [303,] 0.067080405
## [304,] 0.073205863
## [305,] 0.146430503
## [306,] 0.150696636
## [307,] 0.089491012
## [308,] 0.054275695
## [309,] 0.052005206
## [310,] 0.075319746
## [311,] 0.108031809
## [312,] 0.104369882
## [313,] 0.109826230
## [314,] 0.145909380
## [315,] 0.067300235
## [316,] 0.110307748
## [317,] 0.170965484
## [318,] 0.125494957
## [319,] 0.077534303
## [320,] 0.105805341
## [321,] 0.101460408
## [322,] 0.048687911
## [323,] 0.119219369
## [324,] 0.118374093
## [325,] 0.051313120
## [326,] 0.104587886
## [327,] 0.156090948
## [328,] 0.080881352
## [329,] 0.071275554
## [330,] 0.169334732
## [331,] 0.071029060
## [332,] 0.109901795
## [333,] 0.072372229
## [334,] 0.074452115
## [335,] 0.051744627
## [336,] 0.088968854
## [337,] 0.131752165
## [338,] 0.079780030
## [339,] 0.049616312
## [340,] 0.119843890
## [341,] 0.132143299
## [342,] 0.115141570
## [343,] 0.098638041
## [344,] 0.111180004
## [345,] 0.071518244
## [346,] 0.131386017
## [347,] 0.124404084
## [348,] 0.121805159
## [349,] 0.059881323
## [350,] 0.026197204
## [351,] 0.068842999
## [352,] 0.113242533
## [353,] 0.051231943
## [354,] 0.113220485
## [355,] 0.078371515
## [356,] 0.144721550
## [357,] 0.077144937
## [358,] 0.093739371
## [359,] 0.146103164
## [360,] 0.356210248
## [361,] 0.098728668
## [362,] 0.109489163
## [363,] 0.072264437
## [364,] 0.043730034
## [365,] 0.033213264
## [366,] 0.064354425
## [367,] 0.042498390
## [368,] 0.084071566
## [369,] 0.068419277
## [370,] 0.118421417
## [371,] 0.088716310
## [372,] 0.103280328
## [373,] 0.073703777
## [374,] 0.200757346
## [375,] 0.095769177
## [376,] 0.115453223
## [377,] 0.118396137
## wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis
## [1,] 0.422372878
## [2,] 0.401722315
## [3,] 0.223275653
## [4,] 0.208201111
## [5,] 0.834947294
## [6,] 0.331101541
## [7,] 0.213896991
## [8,] 0.215775629
## [9,] 0.268141561
## [10,] 0.372371688
## [11,] 0.253719919
## [12,] 0.407602412
## [13,] 0.274233418
## [14,] 0.240670362
## [15,] 0.158334202
## [16,] 0.303780583
## [17,] 0.398821300
## [18,] 0.467931600
## [19,] 0.370420757
## [20,] 0.409661329
## [21,] 0.166295548
## [22,] 0.400917624
## [23,] 0.370324062
## [24,] 0.305097604
## [25,] 0.442426007
## [26,] 0.311995225
## [27,] 0.472902312
## [28,] 1.000000000
## [29,] 0.412496831
## [30,] 0.346916354
## [31,] 0.492427848
## [32,] 0.261163639
## [33,] 0.376431062
## [34,] 0.257357574
## [35,] 0.297485594
## [36,] 0.457580044
## [37,] 0.485628144
## [38,] 0.420893168
## [39,] 0.471998419
## [40,] 0.182789712
## [41,] 0.353465517
## [42,] 0.116033600
## [43,] 0.211070841
## [44,] 0.430037047
## [45,] 0.239886163
## [46,] 0.637298466
## [47,] 0.325706797
## [48,] 0.235137907
## [49,] 0.250386791
## [50,] 0.285134122
## [51,] 0.373586678
## [52,] 0.245398205
## [53,] 0.085021156
## [54,] 0.212523941
## [55,] 0.286555531
## [56,] 0.262137736
## [57,] 0.423278851
## [58,] 0.272280209
## [59,] 0.247338205
## [60,] 0.285381736
## [61,] 0.198248794
## [62,] 0.254107062
## [63,] 0.295907403
## [64,] 0.393038590
## [65,] 0.468525082
## [66,] 0.116547614
## [67,] 0.352354561
## [68,] 0.051578503
## [69,] 0.273657688
## [70,] 0.146308543
## [71,] 0.135785254
## [72,] 0.168241041
## [73,] 0.171442123
## [74,] 0.072450494
## [75,] 0.237515736
## [76,] 0.153540591
## [77,] 0.667845379
## [78,] 0.516973755
## [79,] 0.507344484
## [80,] 0.908364063
## [81,] 0.368706846
## [82,] 0.399793898
## [83,] 0.087233162
## [84,] 0.372352615
## [85,] 0.360673785
## [86,] 0.075487835
## [87,] 0.130586864
## [88,] 0.318379890
## [89,] 0.619879625
## [90,] 0.049117867
## [91,] 0.429380019
## [92,] 0.036389557
## [93,] 0.313971012
## [94,] 0.078354629
## [95,] 0.272499157
## [96,] 0.210896133
## [97,] 0.093975474
## [98,] 0.388766601
## [99,] 0.419025719
## [100,] 0.295727790
## [101,] 0.089993170
## [102,] 0.086422428
## [103,] 0.244713187
## [104,] 0.331055454
## [105,] 0.098254606
## [106,] 0.244623977
## [107,] 0.347669076
## [108,] 0.190320818
## [109,] 0.125603978
## [110,] 0.149275130
## [111,] 0.180188321
## [112,] 0.381003048
## [113,] 0.151787927
## [114,] 0.380516095
## [115,] 0.037584870
## [116,] 0.204521308
## [117,] 0.606088257
## [118,] 0.082246978
## [119,] 0.477742132
## [120,] 0.477548905
## [121,] 0.330464642
## [122,] 0.299205383
## [123,] 0.259929429
## [124,] 0.268774217
## [125,] 0.151538769
## [126,] 0.247686231
## [127,] 0.383737973
## [128,] 0.467462127
## [129,] 0.085958990
## [130,] 0.394191058
## [131,] 0.128981238
## [132,] 0.123806324
## [133,] 0.378656893
## [134,] 0.000000000
## [135,] 0.146224508
## [136,] 0.634330163
## [137,] 0.163162981
## [138,] 0.221140341
## [139,] 0.375727703
## [140,] 0.169134685
## [141,] 0.063994833
## [142,] 0.219629345
## [143,] 0.121438673
## [144,] 0.415842022
## [145,] 0.168597829
## [146,] 0.190846815
## [147,] 0.438040016
## [148,] 0.585565110
## [149,] 0.103001589
## [150,] 0.337734470
## [151,] 0.230503407
## [152,] 0.285871024
## [153,] 0.041478995
## [154,] 0.425717618
## [155,] 0.241659940
## [156,] 0.732570161
## [157,] 0.150249470
## [158,] 0.062569539
## [159,] 0.165884003
## [160,] 0.328928464
## [161,] 0.165134800
## [162,] 0.013298616
## [163,] 0.272605338
## [164,] 0.302967012
## [165,] 0.328141438
## [166,] 0.119576438
## [167,] 0.270000802
## [168,] 0.069081211
## [169,] 0.274676297
## [170,] 0.198201698
## [171,] 0.236821998
## [172,] 0.301362434
## [173,] 0.399056637
## [174,] 0.371778917
## [175,] 0.181635468
## [176,] 0.339742713
## [177,] 0.092880217
## [178,] 0.477134914
## [179,] 0.113430318
## [180,] 0.277116015
## [181,] 0.241355057
## [182,] 0.189245236
## [183,] 0.625868603
## [184,] 0.163212697
## [185,] 0.075718756
## [186,] 0.178131285
## [187,] 0.319629086
## [188,] 0.115172328
## [189,] 0.104511839
## [190,] 0.432826202
## [191,] 0.344441485
## [192,] 0.339565982
## [193,] 0.270426316
## [194,] 0.419484002
## [195,] 0.218550354
## [196,] 0.237739860
## [197,] 0.357472126
## [198,] 0.246149587
## [199,] 0.184543014
## [200,] 0.337472061
## [201,] 0.249533760
## [202,] 0.321801621
## [203,] 0.835216553
## [204,] 0.131960701
## [205,] 0.321777579
## [206,] 0.246626522
## [207,] 0.136383389
## [208,] 0.292608159
## [209,] 0.128723675
## [210,] 0.227702809
## [211,] 0.239283800
## [212,] 0.408040048
## [213,] 0.136317432
## [214,] 0.277508499
## [215,] 0.136620634
## [216,] 0.147510472
## [217,] 0.466933884
## [218,] 0.156479669
## [219,] 0.268791381
## [220,] 0.031371913
## [221,] 0.067491566
## [222,] 0.083964305
## [223,] 0.030603681
## [224,] 0.126623383
## [225,] 0.439809482
## [226,] 0.439132807
## [227,] 0.357102124
## [228,] 0.308172809
## [229,] 0.144134151
## [230,] 0.430646464
## [231,] 0.339655846
## [232,] 0.263393031
## [233,] 0.183356654
## [234,] 0.192065165
## [235,] 0.222792369
## [236,] 0.316212644
## [237,] 0.190904022
## [238,] 0.067016481
## [239,] 0.232029980
## [240,] 0.201475790
## [241,] 0.494269665
## [242,] 0.327795605
## [243,] 0.265114777
## [244,] 0.316615926
## [245,] 0.159610139
## [246,] 0.333219771
## [247,] 0.261435710
## [248,] 0.191752115
## [249,] 0.019695616
## [250,] 0.384044605
## [251,] 0.173997861
## [252,] 0.261634706
## [253,] 0.181577300
## [254,] 0.025403395
## [255,] 0.271448227
## [256,] 0.356106993
## [257,] 0.374366848
## [258,] 0.452605110
## [259,] 0.199268283
## [260,] 0.166339721
## [261,] 0.079027916
## [262,] 0.183608636
## [263,] 0.257201512
## [264,] 0.139752683
## [265,] 0.417050255
## [266,] 0.282624741
## [267,] 0.366928341
## [268,] 0.369981980
## [269,] 0.448803291
## [270,] 0.463806031
## [271,] 0.056245551
## [272,] 0.328173546
## [273,] 0.377007143
## [274,] 0.045889767
## [275,] 0.940627040
## [276,] 0.236241031
## [277,] 0.210616893
## [278,] 0.278253309
## [279,] 0.212744053
## [280,] 0.198017998
## [281,] 0.343957237
## [282,] 0.371731930
## [283,] 0.133576369
## [284,] 0.269090450
## [285,] 0.287353145
## [286,] 0.450466284
## [287,] 0.077587959
## [288,] 0.029740000
## [289,] 0.464733457
## [290,] 0.185171389
## [291,] 0.236738334
## [292,] 0.284768652
## [293,] 0.138236524
## [294,] 0.069968640
## [295,] 0.203439301
## [296,] 0.442678106
## [297,] 0.248003221
## [298,] 0.206524102
## [299,] 0.351033134
## [300,] 0.188329097
## [301,] 0.185805758
## [302,] 0.566205811
## [303,] 0.457003967
## [304,] 0.252475426
## [305,] 0.080107219
## [306,] 0.046742467
## [307,] 0.422766766
## [308,] 0.279547241
## [309,] 0.113841478
## [310,] 0.189790588
## [311,] 0.360532459
## [312,] 0.209686683
## [313,] 0.188675022
## [314,] 0.284317948
## [315,] 0.307454546
## [316,] 0.353453983
## [317,] 0.088680358
## [318,] 0.234867399
## [319,] 0.291336818
## [320,] 0.271892590
## [321,] 0.136125662
## [322,] 0.261480641
## [323,] 0.331514917
## [324,] 0.105646318
## [325,] 0.245004653
## [326,] 0.403026185
## [327,] 0.066528075
## [328,] 0.127299941
## [329,] 0.193034229
## [330,] 0.045700549
## [331,] 0.345505541
## [332,] 0.284834388
## [333,] 0.348086429
## [334,] 0.306364233
## [335,] 0.395937529
## [336,] 0.246214225
## [337,] 0.112403653
## [338,] 0.416248668
## [339,] 0.236227848
## [340,] 0.315451269
## [341,] 0.167959405
## [342,] 0.279524792
## [343,] 0.071286227
## [344,] 0.030048161
## [345,] 0.397431523
## [346,] 0.001253733
## [347,] 0.170012839
## [348,] 0.276764604
## [349,] 0.184889584
## [350,] 0.203811594
## [351,] 0.232801660
## [352,] 0.092689323
## [353,] 0.360392192
## [354,] 0.233292712
## [355,] 0.283300461
## [356,] 0.129423098
## [357,] 0.519222293
## [358,] 0.332470969
## [359,] 0.345685363
## [360,] 0.367946785
## [361,] 0.263619394
## [362,] 0.273760728
## [363,] 0.219632594
## [364,] 0.310744058
## [365,] 0.488854732
## [366,] 0.339179489
## [367,] 0.133067722
## [368,] 0.268445037
## [369,] 0.093946161
## [370,] 0.100252075
## [371,] 0.033759726
## [372,] 0.406330868
## [373,] 0.405397674
## [374,] 0.920700952
## [375,] 0.452914797
## [376,] 0.166222491
## [377,] 0.239394180
## wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis
## [1,] 0.83221562
## [2,] 0.84411604
## [3,] 0.51991797
## [4,] 0.77558235
## [5,] 0.70352819
## [6,] 0.55092303
## [7,] 0.48881080
## [8,] 0.52939814
## [9,] 0.68178814
## [10,] 0.50228231
## [11,] 0.64416975
## [12,] 0.69210811
## [13,] 0.50541875
## [14,] 0.44519278
## [15,] 0.32041344
## [16,] 0.74002257
## [17,] 0.69518463
## [18,] 0.80580097
## [19,] 0.80625235
## [20,] 0.67645301
## [21,] 0.52413824
## [22,] 0.67548983
## [23,] 0.82562925
## [24,] 0.68371475
## [25,] 0.79333648
## [26,] 0.64238759
## [27,] 0.73412032
## [28,] 0.76204127
## [29,] 0.74201282
## [30,] 0.73318461
## [31,] 0.61674400
## [32,] 0.38532567
## [33,] 0.75181376
## [34,] 0.86583246
## [35,] 0.69649947
## [36,] 0.86058985
## [37,] 0.90132886
## [38,] 0.52585169
## [39,] 0.78334311
## [40,] 0.68986039
## [41,] 0.54550095
## [42,] 0.24372978
## [43,] 0.29470683
## [44,] 0.70503976
## [45,] 0.79171951
## [46,] 0.72086221
## [47,] 0.65771226
## [48,] 0.66279585
## [49,] 0.79015799
## [50,] 0.60527527
## [51,] 0.61298228
## [52,] 0.86234561
## [53,] 0.38196573
## [54,] 0.22393621
## [55,] 0.50668574
## [56,] 0.54866772
## [57,] 0.86716310
## [58,] 0.62722787
## [59,] 0.76443698
## [60,] 0.65015980
## [61,] 0.63553192
## [62,] 0.66085713
## [63,] 0.78592574
## [64,] 0.76835850
## [65,] 0.70663209
## [66,] 0.43419772
## [67,] 0.69817975
## [68,] 0.46471691
## [69,] 0.67536077
## [70,] 0.43363489
## [71,] 0.37071161
## [72,] 0.34953134
## [73,] 0.47794404
## [74,] 0.12892636
## [75,] 0.48631624
## [76,] 0.15321856
## [77,] 1.00000000
## [78,] 0.73197668
## [79,] 0.70905687
## [80,] 0.85873801
## [81,] 0.69678308
## [82,] 0.90943918
## [83,] 0.19775268
## [84,] 0.68696342
## [85,] 0.63683474
## [86,] 0.44827641
## [87,] 0.43176747
## [88,] 0.81468173
## [89,] 0.80614587
## [90,] 0.31844930
## [91,] 0.48397534
## [92,] 0.16149957
## [93,] 0.70126325
## [94,] 0.16863744
## [95,] 0.44415949
## [96,] 0.67503224
## [97,] 0.26741554
## [98,] 0.75999710
## [99,] 0.76220765
## [100,] 0.59462749
## [101,] 0.41020237
## [102,] 0.16176890
## [103,] 0.62365912
## [104,] 0.65752756
## [105,] 0.43160602
## [106,] 0.65180450
## [107,] 0.47569864
## [108,] 0.58932980
## [109,] 0.32836969
## [110,] 0.47841977
## [111,] 0.62684514
## [112,] 0.74777249
## [113,] 0.50919203
## [114,] 0.73786117
## [115,] 0.25850029
## [116,] 0.35373957
## [117,] 0.78589277
## [118,] 0.34055997
## [119,] 0.87676054
## [120,] 0.79012948
## [121,] 0.89201244
## [122,] 0.74353974
## [123,] 0.90905431
## [124,] 0.63557216
## [125,] 0.46196837
## [126,] 0.58213977
## [127,] 0.67712213
## [128,] 0.99962282
## [129,] 0.41797198
## [130,] 0.60412289
## [131,] 0.43431470
## [132,] 0.37447024
## [133,] 0.67098557
## [134,] 0.08472650
## [135,] 0.17628065
## [136,] 0.61527802
## [137,] 0.31735113
## [138,] 0.49948598
## [139,] 0.75360474
## [140,] 0.61433360
## [141,] 0.39691232
## [142,] 0.62793990
## [143,] 0.17377873
## [144,] 0.71341118
## [145,] 0.58607339
## [146,] 0.66042993
## [147,] 0.76505879
## [148,] 0.72159208
## [149,] 0.44532872
## [150,] 0.63435557
## [151,] 0.39186966
## [152,] 0.55996608
## [153,] 0.21988106
## [154,] 0.85622639
## [155,] 0.51498529
## [156,] 0.76368690
## [157,] 0.29237303
## [158,] 0.24580515
## [159,] 0.69149599
## [160,] 0.80680138
## [161,] 0.54805733
## [162,] 0.27920648
## [163,] 0.61426652
## [164,] 0.85279679
## [165,] 0.57588850
## [166,] 0.17875699
## [167,] 0.80644963
## [168,] 0.19530008
## [169,] 0.78147751
## [170,] 0.55533034
## [171,] 0.61771028
## [172,] 0.78081155
## [173,] 0.79148703
## [174,] 0.85808528
## [175,] 0.37263457
## [176,] 0.61870456
## [177,] 0.51862751
## [178,] 0.63034128
## [179,] 0.36406328
## [180,] 0.67378806
## [181,] 0.63665295
## [182,] 0.63958730
## [183,] 0.62400893
## [184,] 0.73805356
## [185,] 0.42450288
## [186,] 0.48826091
## [187,] 0.65397853
## [188,] 0.22948458
## [189,] 0.37079920
## [190,] 0.76098635
## [191,] 0.79424559
## [192,] 0.61266821
## [193,] 0.70514635
## [194,] 0.75995619
## [195,] 0.43730264
## [196,] 0.46995213
## [197,] 0.70396480
## [198,] 0.64279957
## [199,] 0.56766030
## [200,] 0.61086070
## [201,] 0.68467365
## [202,] 0.81555150
## [203,] 0.63751052
## [204,] 0.33560007
## [205,] 0.63017310
## [206,] 0.81077123
## [207,] 0.44742212
## [208,] 0.71194343
## [209,] 0.70543428
## [210,] 0.74195901
## [211,] 0.67127740
## [212,] 0.75605585
## [213,] 0.49273018
## [214,] 0.65052566
## [215,] 0.48926076
## [216,] 0.56448154
## [217,] 0.78095286
## [218,] 0.49805825
## [219,] 0.65943767
## [220,] 0.28443086
## [221,] 0.45249106
## [222,] 0.32559811
## [223,] 0.25740093
## [224,] 0.44278523
## [225,] 0.69379650
## [226,] 0.82157691
## [227,] 0.68639016
## [228,] 0.69849826
## [229,] 0.49877754
## [230,] 0.76418830
## [231,] 0.81152559
## [232,] 0.65215937
## [233,] 0.53448306
## [234,] 0.47389904
## [235,] 0.67704295
## [236,] 0.59816089
## [237,] 0.60550418
## [238,] 0.11975343
## [239,] 0.53941425
## [240,] 0.39987133
## [241,] 0.88493592
## [242,] 0.58854014
## [243,] 0.78345182
## [244,] 0.84399121
## [245,] 0.56445391
## [246,] 0.63266069
## [247,] 0.70271313
## [248,] 0.26747066
## [249,] 0.00000000
## [250,] 0.83843146
## [251,] 0.59147802
## [252,] 0.79277721
## [253,] 0.74083632
## [254,] 0.02139782
## [255,] 0.80265392
## [256,] 0.76921703
## [257,] 0.68617409
## [258,] 0.78918456
## [259,] 0.62768935
## [260,] 0.60309916
## [261,] 0.26788339
## [262,] 0.35413836
## [263,] 0.60664136
## [264,] 0.51840315
## [265,] 0.61841346
## [266,] 0.72599014
## [267,] 0.76986358
## [268,] 0.65130636
## [269,] 0.79919942
## [270,] 0.71387986
## [271,] 0.18421191
## [272,] 0.83742082
## [273,] 0.81108882
## [274,] 0.13614043
## [275,] 0.71068479
## [276,] 0.48487000
## [277,] 0.76877079
## [278,] 0.82261061
## [279,] 0.49101712
## [280,] 0.52047660
## [281,] 0.56752106
## [282,] 0.77997636
## [283,] 0.32265155
## [284,] 0.53427771
## [285,] 0.54027267
## [286,] 0.71220154
## [287,] 0.20357449
## [288,] 0.06339411
## [289,] 0.72915432
## [290,] 0.43165934
## [291,] 0.55594333
## [292,] 0.77223606
## [293,] 0.40796015
## [294,] 0.38751401
## [295,] 0.86575449
## [296,] 0.63931050
## [297,] 0.57434695
## [298,] 0.65655441
## [299,] 0.79059199
## [300,] 0.46960862
## [301,] 0.59581907
## [302,] 0.80055447
## [303,] 0.77342789
## [304,] 0.74076169
## [305,] 0.36265602
## [306,] 0.27746486
## [307,] 0.80812260
## [308,] 0.95565561
## [309,] 0.54107412
## [310,] 0.68733268
## [311,] 0.68274871
## [312,] 0.49636697
## [313,] 0.51722032
## [314,] 0.47572815
## [315,] 0.76443031
## [316,] 0.58778008
## [317,] 0.26114392
## [318,] 0.77048464
## [319,] 0.71368680
## [320,] 0.48474269
## [321,] 0.45593270
## [322,] 0.73213230
## [323,] 0.39322842
## [324,] 0.29363019
## [325,] 0.92530242
## [326,] 0.75815876
## [327,] 0.30386287
## [328,] 0.55407590
## [329,] 0.32744662
## [330,] 0.09602557
## [331,] 0.61620854
## [332,] 0.60816228
## [333,] 0.90318367
## [334,] 0.76354390
## [335,] 0.92779968
## [336,] 0.48398872
## [337,] 0.42887888
## [338,] 0.57226668
## [339,] 0.65999690
## [340,] 0.65080726
## [341,] 0.29836754
## [342,] 0.53412702
## [343,] 0.28272811
## [344,] 0.07358503
## [345,] 0.84692835
## [346,] 0.27822459
## [347,] 0.52524920
## [348,] 0.59209826
## [349,] 0.48820700
## [350,] 0.60694063
## [351,] 0.80779219
## [352,] 0.26770564
## [353,] 0.97004263
## [354,] 0.69761188
## [355,] 0.52289830
## [356,] 0.15297801
## [357,] 0.82541096
## [358,] 0.72644914
## [359,] 0.48948799
## [360,] 0.38604914
## [361,] 0.59712406
## [362,] 0.53370952
## [363,] 0.67536380
## [364,] 0.55467850
## [365,] 0.70290913
## [366,] 0.74253647
## [367,] 0.47140541
## [368,] 0.63730039
## [369,] 0.29803345
## [370,] 0.45742434
## [371,] 0.16872093
## [372,] 0.68132915
## [373,] 0.79679642
## [374,] 0.70075748
## [375,] 0.77104080
## [376,] 0.53064939
## [377,] 0.58692620
## wavelet.LLL_glcm_ClusterProminence
## [1,] 0.72469861
## [2,] 0.75952533
## [3,] 0.63697174
## [4,] 0.62094507
## [5,] 0.64517497
## [6,] 0.78905187
## [7,] 0.54893442
## [8,] 0.65992886
## [9,] 0.62205683
## [10,] 0.75448763
## [11,] 0.61385315
## [12,] 0.68093329
## [13,] 0.65093657
## [14,] 0.86587208
## [15,] 0.68526696
## [16,] 0.60998255
## [17,] 0.68211069
## [18,] 0.73445796
## [19,] 0.55494121
## [20,] 0.78178515
## [21,] 0.61149858
## [22,] 0.82532900
## [23,] 0.53199806
## [24,] 0.56536902
## [25,] 0.77377177
## [26,] 0.73740791
## [27,] 0.70027260
## [28,] 0.77357657
## [29,] 0.38092449
## [30,] 0.64077218
## [31,] 0.87438569
## [32,] 0.76404032
## [33,] 0.63850313
## [34,] 0.57576858
## [35,] 0.39068855
## [36,] 0.48877281
## [37,] 0.60978487
## [38,] 0.37735656
## [39,] 0.67438346
## [40,] 0.65085933
## [41,] 0.82556939
## [42,] 0.63101313
## [43,] 0.53675045
## [44,] 0.71474460
## [45,] 0.65340320
## [46,] 0.63659757
## [47,] 0.75256164
## [48,] 0.47225630
## [49,] 0.45548454
## [50,] 0.78069024
## [51,] 0.73193940
## [52,] 0.53023751
## [53,] 0.60865353
## [54,] 0.41482515
## [55,] 0.58017985
## [56,] 0.56979967
## [57,] 0.23981567
## [58,] 0.39308866
## [59,] 0.64382742
## [60,] 0.70121031
## [61,] 0.56470182
## [62,] 0.59218382
## [63,] 0.58852485
## [64,] 0.62540392
## [65,] 0.38245488
## [66,] 0.77767434
## [67,] 0.70872241
## [68,] 0.63960369
## [69,] 0.70426913
## [70,] 0.53293939
## [71,] 0.79893954
## [72,] 0.82502132
## [73,] 0.60777454
## [74,] 0.47167346
## [75,] 0.68655183
## [76,] 0.75942476
## [77,] 0.48259287
## [78,] 0.47098779
## [79,] 0.60936947
## [80,] 0.45289065
## [81,] 0.70332152
## [82,] 0.68206658
## [83,] 0.67441710
## [84,] 0.76549128
## [85,] 0.75624861
## [86,] 0.63014515
## [87,] 0.70058789
## [88,] 0.66242754
## [89,] 0.64171694
## [90,] 0.47558580
## [91,] 0.38677551
## [92,] 0.58941514
## [93,] 0.66647040
## [94,] 0.74131522
## [95,] 0.35393840
## [96,] 0.61636328
## [97,] 0.65343834
## [98,] 0.75495514
## [99,] 0.61058120
## [100,] 0.28895307
## [101,] 0.80709415
## [102,] 0.80945576
## [103,] 0.74928487
## [104,] 0.65584074
## [105,] 0.48306864
## [106,] 0.67701998
## [107,] 0.86722915
## [108,] 0.70530141
## [109,] 0.70125276
## [110,] 0.69716877
## [111,] 0.24907472
## [112,] 0.51580555
## [113,] 0.60704195
## [114,] 0.52571457
## [115,] 0.59613941
## [116,] 0.38234860
## [117,] 0.67510863
## [118,] 0.63845515
## [119,] 0.63938462
## [120,] 0.48423193
## [121,] 0.52332010
## [122,] 0.34913551
## [123,] 0.49233782
## [124,] 0.43121292
## [125,] 0.70899526
## [126,] 0.59905787
## [127,] 0.64886789
## [128,] 0.18673811
## [129,] 0.54435116
## [130,] 0.88510106
## [131,] 0.76234441
## [132,] 0.79293726
## [133,] 0.77265355
## [134,] 0.57492525
## [135,] 0.93937427
## [136,] 0.72733694
## [137,] 0.86824926
## [138,] 0.66600061
## [139,] 0.77404049
## [140,] 0.57613144
## [141,] 0.72701903
## [142,] 0.60246686
## [143,] 0.32167663
## [144,] 0.81914717
## [145,] 0.72481740
## [146,] 0.49046809
## [147,] 0.51982188
## [148,] 0.73867279
## [149,] 0.66410512
## [150,] 0.52113874
## [151,] 0.65177248
## [152,] 0.57309003
## [153,] 0.70418225
## [154,] 0.64574817
## [155,] 0.58993928
## [156,] 0.65829703
## [157,] 0.16975924
## [158,] 0.43444117
## [159,] 0.71233250
## [160,] 0.44935796
## [161,] 0.55836558
## [162,] 0.51585223
## [163,] 0.72697930
## [164,] 0.50555592
## [165,] 0.86311811
## [166,] 0.65109050
## [167,] 0.58323982
## [168,] 0.66416939
## [169,] 0.83440660
## [170,] 0.75747647
## [171,] 0.58007492
## [172,] 0.55612064
## [173,] 0.49955058
## [174,] 0.53489205
## [175,] 0.65217613
## [176,] 0.61267752
## [177,] 0.63709053
## [178,] 0.89043886
## [179,] 0.77076336
## [180,] 0.66864034
## [181,] 0.75550256
## [182,] 0.54372103
## [183,] 0.63102457
## [184,] 0.61994511
## [185,] 0.55800747
## [186,] 0.62668393
## [187,] 0.64333202
## [188,] 0.65589327
## [189,] 0.61905864
## [190,] 0.48274378
## [191,] 0.47717320
## [192,] 0.38852296
## [193,] 0.59000005
## [194,] 0.61336545
## [195,] 0.96880777
## [196,] 0.49529386
## [197,] 0.75277549
## [198,] 0.46855882
## [199,] 0.46221935
## [200,] 0.84452460
## [201,] 0.72106321
## [202,] 0.33923043
## [203,] 0.72895731
## [204,] 0.67174586
## [205,] 0.60497676
## [206,] 0.54110310
## [207,] 0.62937111
## [208,] 0.56404616
## [209,] 0.35361283
## [210,] 0.74707716
## [211,] 0.54174318
## [212,] 0.58953846
## [213,] 0.58057345
## [214,] 0.40727695
## [215,] 0.60039435
## [216,] 0.29292156
## [217,] 0.38374568
## [218,] 0.69858804
## [219,] 0.89713272
## [220,] 0.52199095
## [221,] 0.59206822
## [222,] 0.51309567
## [223,] 0.65852930
## [224,] 0.63843563
## [225,] 0.42583536
## [226,] 0.48428410
## [227,] 0.57831754
## [228,] 0.49682134
## [229,] 0.55697949
## [230,] 0.58805293
## [231,] 0.37094822
## [232,] 0.46084062
## [233,] 0.68342630
## [234,] 0.68438078
## [235,] 0.38683585
## [236,] 0.69180386
## [237,] 0.43138103
## [238,] 0.64099598
## [239,] 0.54618720
## [240,] 0.48253860
## [241,] 1.00000000
## [242,] 0.54276974
## [243,] 0.50538285
## [244,] 0.35528812
## [245,] 0.49479537
## [246,] 0.30791822
## [247,] 0.46303535
## [248,] 0.53054742
## [249,] 0.48685462
## [250,] 0.36113194
## [251,] 0.66473458
## [252,] 0.35990807
## [253,] 0.59915485
## [254,] 0.52905193
## [255,] 0.52894843
## [256,] 0.59338875
## [257,] 0.67000818
## [258,] 0.30547309
## [259,] 0.73475558
## [260,] 0.66834945
## [261,] 0.59462228
## [262,] 0.22541800
## [263,] 0.66190061
## [264,] 0.73422756
## [265,] 0.49664999
## [266,] 0.66147717
## [267,] 0.68883867
## [268,] 0.70146267
## [269,] 0.87949308
## [270,] 0.68623918
## [271,] 0.69114365
## [272,] 0.43247106
## [273,] 0.35035668
## [274,] 0.68232184
## [275,] 0.80383418
## [276,] 0.70284417
## [277,] 0.24164588
## [278,] 0.61655998
## [279,] 0.65961208
## [280,] 0.53629010
## [281,] 0.81311047
## [282,] 0.76332935
## [283,] 0.70968200
## [284,] 0.55287718
## [285,] 0.03391486
## [286,] 0.71519857
## [287,] 0.58856036
## [288,] 0.62421692
## [289,] 0.53307620
## [290,] 0.64022394
## [291,] 0.69185631
## [292,] 0.51610086
## [293,] 0.48104177
## [294,] 0.70476373
## [295,] 0.28347919
## [296,] 0.16114423
## [297,] 0.61282047
## [298,] 0.42648540
## [299,] 0.37891084
## [300,] 0.52482106
## [301,] 0.50339502
## [302,] 0.24536001
## [303,] 0.32070272
## [304,] 0.55870404
## [305,] 0.62683685
## [306,] 0.57513243
## [307,] 0.93501439
## [308,] 0.22434941
## [309,] 0.51578504
## [310,] 0.41632835
## [311,] 0.72972225
## [312,] 0.60665464
## [313,] 0.63210329
## [314,] 0.60339962
## [315,] 0.35910187
## [316,] 0.59948715
## [317,] 0.68639207
## [318,] 0.68610633
## [319,] 0.49041364
## [320,] 0.49065487
## [321,] 0.45447727
## [322,] 0.10056061
## [323,] 0.44588902
## [324,] 0.71050581
## [325,] 0.00000000
## [326,] 0.66728288
## [327,] 0.53371061
## [328,] 0.28640364
## [329,] 0.68552713
## [330,] 0.73709555
## [331,] 0.59864762
## [332,] 0.49118271
## [333,] 0.47768598
## [334,] 0.40653150
## [335,] 0.27028269
## [336,] 0.43443617
## [337,] 0.61577915
## [338,] 0.46930880
## [339,] 0.18902329
## [340,] 0.63246897
## [341,] 0.57009610
## [342,] 0.55775717
## [343,] 0.38247001
## [344,] 0.38758279
## [345,] 0.55999750
## [346,] 0.61732148
## [347,] 0.45680686
## [348,] 0.61580126
## [349,] 0.59722461
## [350,] 0.46721561
## [351,] 0.39236749
## [352,] 0.33216174
## [353,] 0.11123150
## [354,] 0.50134713
## [355,] 0.67548940
## [356,] 0.67088916
## [357,] 0.49832884
## [358,] 0.60165828
## [359,] 0.74316216
## [360,] 0.88140441
## [361,] 0.54216205
## [362,] 0.49670840
## [363,] 0.62631338
## [364,] 0.52946123
## [365,] 0.49082643
## [366,] 0.44921504
## [367,] 0.56793686
## [368,] 0.54769105
## [369,] 0.60599013
## [370,] 0.49680600
## [371,] 0.62702966
## [372,] 0.63767196
## [373,] 0.40723562
## [374,] 0.73180318
## [375,] 0.53126285
## [376,] 0.54144605
## [377,] 0.74851208
## wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis
## [1,] 0.084781518
## [2,] 0.030619849
## [3,] 0.027006600
## [4,] 0.027246886
## [5,] 0.019438958
## [6,] 0.034990154
## [7,] 0.258867791
## [8,] 0.020186564
## [9,] 0.048286376
## [10,] 0.040586066
## [11,] 0.023534627
## [12,] 0.031649301
## [13,] 0.041822393
## [14,] 0.043765291
## [15,] 0.105110004
## [16,] 0.042702924
## [17,] 0.017963872
## [18,] 0.024501577
## [19,] 0.024805003
## [20,] 0.035015211
## [21,] 0.100412829
## [22,] 0.019318579
## [23,] 0.035640844
## [24,] 0.048776187
## [25,] 0.021426197
## [26,] 0.042621971
## [27,] 0.066302934
## [28,] 0.001120832
## [29,] 0.116925579
## [30,] 0.062027727
## [31,] 0.048515052
## [32,] 0.048040388
## [33,] 0.027220172
## [34,] 0.075530716
## [35,] 0.015269678
## [36,] 0.008213660
## [37,] 0.040532580
## [38,] 0.060443306
## [39,] 0.045118547
## [40,] 0.052058532
## [41,] 0.035267739
## [42,] 0.070017050
## [43,] 0.090775842
## [44,] 0.019570287
## [45,] 0.026498456
## [46,] 0.025140551
## [47,] 0.055878643
## [48,] 0.064368793
## [49,] 0.044879951
## [50,] 0.028419987
## [51,] 0.045168414
## [52,] 0.038788102
## [53,] 0.229159514
## [54,] 0.172104962
## [55,] 0.062626046
## [56,] 0.088648799
## [57,] 0.032084824
## [58,] 0.155582517
## [59,] 0.047230041
## [60,] 0.060797530
## [61,] 0.024811760
## [62,] 0.016040533
## [63,] 0.040401973
## [64,] 0.061539000
## [65,] 0.049528189
## [66,] 0.098007514
## [67,] 0.078452084
## [68,] 0.228212177
## [69,] 0.041710017
## [70,] 0.167338567
## [71,] 0.182596717
## [72,] 0.054559584
## [73,] 0.129973331
## [74,] 0.279279847
## [75,] 0.062248980
## [76,] 0.207661821
## [77,] 0.037093752
## [78,] 0.057143570
## [79,] 0.082742012
## [80,] 0.037428216
## [81,] 0.056543924
## [82,] 0.017920129
## [83,] 0.170745089
## [84,] 0.033787508
## [85,] 0.029857316
## [86,] 0.072294969
## [87,] 0.201063382
## [88,] 0.045953182
## [89,] 0.014068187
## [90,] 0.118076147
## [91,] 0.073710396
## [92,] 0.169786030
## [93,] 0.018277351
## [94,] 0.161886903
## [95,] 0.219030452
## [96,] 0.057354226
## [97,] 0.234555398
## [98,] 0.013591321
## [99,] 0.008848792
## [100,] 0.088839602
## [101,] 0.231738413
## [102,] 0.085224390
## [103,] 0.045273422
## [104,] 0.038487865
## [105,] 0.088784450
## [106,] 0.030957259
## [107,] 0.028686474
## [108,] 0.030853484
## [109,] 0.148865468
## [110,] 0.073286863
## [111,] 0.062053382
## [112,] 0.021210205
## [113,] 0.041857441
## [114,] 0.051307242
## [115,] 0.185646733
## [116,] 0.202256638
## [117,] 0.042012856
## [118,] 0.110904970
## [119,] 0.015479663
## [120,] 0.027255415
## [121,] 0.041830173
## [122,] 0.120012323
## [123,] 0.172348279
## [124,] 0.056889690
## [125,] 0.132522971
## [126,] 0.062864666
## [127,] 0.030657952
## [128,] 0.066365868
## [129,] 0.182377589
## [130,] 0.021047745
## [131,] 0.042779483
## [132,] 0.094400837
## [133,] 0.040129866
## [134,] 0.339585093
## [135,] 0.677904898
## [136,] 0.022993712
## [137,] 0.042226862
## [138,] 0.039285645
## [139,] 0.013976660
## [140,] 0.111093618
## [141,] 0.112737796
## [142,] 0.080806497
## [143,] 0.203890483
## [144,] 0.045685241
## [145,] 0.080810791
## [146,] 0.062258427
## [147,] 0.011502354
## [148,] 0.004665331
## [149,] 0.134321689
## [150,] 0.076936504
## [151,] 0.127729023
## [152,] 0.043972599
## [153,] 0.077569451
## [154,] 0.010769664
## [155,] 0.096503193
## [156,] 0.009687360
## [157,] 0.140371446
## [158,] 0.091634110
## [159,] 0.083067740
## [160,] 0.065347294
## [161,] 0.088710190
## [162,] 0.304935559
## [163,] 0.032231147
## [164,] 0.028285480
## [165,] 0.014258441
## [166,] 0.258104244
## [167,] 0.033977208
## [168,] 0.106143680
## [169,] 0.022508207
## [170,] 0.026611989
## [171,] 0.046856934
## [172,] 0.017772269
## [173,] 0.039749232
## [174,] 0.044125408
## [175,] 0.053256522
## [176,] 0.062096180
## [177,] 0.032033126
## [178,] 0.033064281
## [179,] 0.057236226
## [180,] 0.050699415
## [181,] 0.038421758
## [182,] 0.077841978
## [183,] 0.013344170
## [184,] 0.106026581
## [185,] 0.125060581
## [186,] 0.065575262
## [187,] 0.034643664
## [188,] 0.067527357
## [189,] 0.083290888
## [190,] 0.012857427
## [191,] 0.026340930
## [192,] 0.087826888
## [193,] 0.028863210
## [194,] 0.037791826
## [195,] 0.020201725
## [196,] 0.040938018
## [197,] 0.037491396
## [198,] 0.037456640
## [199,] 0.056950241
## [200,] 0.046098165
## [201,] 0.033396385
## [202,] 0.048801719
## [203,] 0.000000000
## [204,] 0.417892083
## [205,] 0.028885917
## [206,] 0.071640470
## [207,] 0.180397166
## [208,] 0.056819732
## [209,] 0.049179611
## [210,] 0.046543728
## [211,] 0.079107368
## [212,] 0.074204784
## [213,] 0.097483682
## [214,] 0.057525938
## [215,] 0.077112567
## [216,] 0.202145533
## [217,] 0.059954629
## [218,] 0.145775297
## [219,] 0.025117318
## [220,] 0.261064646
## [221,] 0.167867934
## [222,] 0.117946435
## [223,] 0.137193074
## [224,] 0.065453203
## [225,] 0.038101262
## [226,] 0.037929101
## [227,] 0.036202996
## [228,] 0.209097446
## [229,] 0.073358483
## [230,] 0.035506684
## [231,] 0.063380777
## [232,] 0.082638221
## [233,] 0.215258578
## [234,] 0.063088227
## [235,] 0.059178012
## [236,] 0.088154950
## [237,] 0.076204411
## [238,] 0.278822109
## [239,] 0.201578934
## [240,] 0.172361099
## [241,] 0.059702535
## [242,] 0.144405410
## [243,] 0.116808601
## [244,] 0.051217059
## [245,] 0.087352296
## [246,] 0.105800218
## [247,] 0.089003024
## [248,] 0.088075356
## [249,] 0.236560064
## [250,] 0.095595120
## [251,] 0.137561170
## [252,] 0.050987097
## [253,] 0.069665653
## [254,] 0.261351731
## [255,] 0.047834618
## [256,] 0.137977910
## [257,] 0.036347913
## [258,] 0.071246020
## [259,] 0.094101985
## [260,] 0.089432963
## [261,] 0.352586902
## [262,] 0.317152008
## [263,] 0.136765376
## [264,] 0.067710311
## [265,] 0.028702295
## [266,] 0.082348742
## [267,] 0.029571074
## [268,] 0.018871569
## [269,] 0.040873398
## [270,] 0.074564343
## [271,] 0.464442691
## [272,] 0.052667198
## [273,] 0.081314818
## [274,] 0.202983011
## [275,] 0.006974022
## [276,] 0.324772227
## [277,] 0.104056052
## [278,] 0.115332667
## [279,] 0.110722852
## [280,] 0.101522640
## [281,] 0.015800004
## [282,] 0.038557948
## [283,] 0.131376384
## [284,] 0.192420995
## [285,] 0.208637195
## [286,] 0.083601541
## [287,] 0.287779027
## [288,] 0.339381563
## [289,] 0.043971689
## [290,] 0.175866904
## [291,] 0.152698466
## [292,] 0.197736670
## [293,] 0.180351676
## [294,] 0.286546650
## [295,] 0.046238072
## [296,] 0.090876464
## [297,] 0.078530655
## [298,] 0.054970190
## [299,] 0.067913893
## [300,] 0.124968531
## [301,] 0.057839664
## [302,] 0.034144230
## [303,] 0.028159291
## [304,] 0.054713155
## [305,] 0.299985468
## [306,] 0.288518767
## [307,] 0.065926638
## [308,] 0.057200856
## [309,] 0.041476407
## [310,] 0.084854642
## [311,] 0.078007293
## [312,] 0.064873480
## [313,] 0.209950832
## [314,] 0.121872763
## [315,] 0.032915033
## [316,] 0.107714444
## [317,] 0.165415206
## [318,] 0.096845178
## [319,] 0.161412831
## [320,] 0.179610228
## [321,] 0.302995617
## [322,] 0.091080899
## [323,] 0.147149861
## [324,] 0.370879196
## [325,] 0.182134076
## [326,] 0.102656154
## [327,] 0.167482350
## [328,] 0.110269335
## [329,] 0.079443758
## [330,] 0.191754685
## [331,] 0.208324698
## [332,] 0.084337805
## [333,] 0.045703896
## [334,] 0.169876395
## [335,] 0.083217596
## [336,] 0.162226546
## [337,] 0.205151337
## [338,] 0.102273570
## [339,] 0.265819421
## [340,] 0.052948612
## [341,] 0.271089750
## [342,] 0.174054182
## [343,] 0.191819501
## [344,] 0.363871223
## [345,] 0.051589893
## [346,] 0.255602739
## [347,] 0.147706689
## [348,] 0.079819169
## [349,] 0.094935053
## [350,] 0.085959363
## [351,] 0.044701649
## [352,] 1.000000000
## [353,] 0.071320658
## [354,] 0.118300920
## [355,] 0.085198499
## [356,] 0.115105828
## [357,] 0.082762805
## [358,] 0.114480476
## [359,] 0.167496819
## [360,] 0.060795847
## [361,] 0.159020564
## [362,] 0.093123318
## [363,] 0.029214888
## [364,] 0.031381439
## [365,] 0.035304021
## [366,] 0.122496374
## [367,] 0.250181203
## [368,] 0.063493787
## [369,] 0.088466801
## [370,] 0.188546383
## [371,] 0.143133128
## [372,] 0.067521823
## [373,] 0.139549773
## [374,] 0.010650600
## [375,] 0.163152670
## [376,] 0.311299389
## [377,] 0.106722047
## wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis
## [1,] 0.0938076472
## [2,] 0.0488819418
## [3,] 0.0734117141
## [4,] 0.0598835876
## [5,] 0.0431015425
## [6,] 0.1037938382
## [7,] 0.2494949480
## [8,] 0.0505271773
## [9,] 0.1122856151
## [10,] 0.0999485897
## [11,] 0.0652857219
## [12,] 0.0754188957
## [13,] 0.0726872828
## [14,] 0.1570212743
## [15,] 0.3187285223
## [16,] 0.0846907199
## [17,] 0.0439931503
## [18,] 0.0370410961
## [19,] 0.0433824852
## [20,] 0.0660812162
## [21,] 0.1891667664
## [22,] 0.0309069145
## [23,] 0.0495735038
## [24,] 0.0530967726
## [25,] 0.0305365125
## [26,] 0.0900044015
## [27,] 0.0386578350
## [28,] 0.0000000000
## [29,] 0.0910501056
## [30,] 0.0734077351
## [31,] 0.0711258114
## [32,] 0.1401546412
## [33,] 0.0388919044
## [34,] 0.0913136452
## [35,] 0.0291517375
## [36,] 0.0109045356
## [37,] 0.0456809667
## [38,] 0.0789887665
## [39,] 0.0560987994
## [40,] 0.1189901623
## [41,] 0.0674492445
## [42,] 0.2828487435
## [43,] 0.2354337221
## [44,] 0.0323645965
## [45,] 0.0524628525
## [46,] 0.0491669452
## [47,] 0.1019483247
## [48,] 0.0928209812
## [49,] 0.0752482218
## [50,] 0.0725244497
## [51,] 0.0591604657
## [52,] 0.0746861574
## [53,] 0.4689372576
## [54,] 0.3802681106
## [55,] 0.1231952722
## [56,] 0.0974196973
## [57,] 0.0195892861
## [58,] 0.1608781310
## [59,] 0.0801934955
## [60,] 0.1131859119
## [61,] 0.0366412741
## [62,] 0.0577377660
## [63,] 0.0438976494
## [64,] 0.0934882623
## [65,] 0.0467821226
## [66,] 0.2385262941
## [67,] 0.1255239827
## [68,] 0.3596533107
## [69,] 0.0801016766
## [70,] 0.3083659059
## [71,] 0.3143249974
## [72,] 0.1618301990
## [73,] 0.1543458156
## [74,] 0.7302917048
## [75,] 0.1311811815
## [76,] 0.8250633484
## [77,] 0.0247091681
## [78,] 0.0660303429
## [79,] 0.0828621379
## [80,] 0.0210817716
## [81,] 0.0847574083
## [82,] 0.0363044374
## [83,] 0.5766364286
## [84,] 0.0606840250
## [85,] 0.0460185826
## [86,] 0.1548559289
## [87,] 0.4081966135
## [88,] 0.0628484976
## [89,] 0.0249633357
## [90,] 0.2817950997
## [91,] 0.0723114884
## [92,] 0.5148498177
## [93,] 0.0355211498
## [94,] 0.4773129020
## [95,] 0.1359218079
## [96,] 0.1265026131
## [97,] 0.5132885236
## [98,] 0.0300751782
## [99,] 0.0182193951
## [100,] 0.1032713076
## [101,] 0.3367602539
## [102,] 0.3174279980
## [103,] 0.0886257908
## [104,] 0.0748871249
## [105,] 0.1886043875
## [106,] 0.0580511380
## [107,] 0.0730173943
## [108,] 0.0773856649
## [109,] 0.3896334302
## [110,] 0.1946362667
## [111,] 0.1360694005
## [112,] 0.0432045875
## [113,] 0.0920161491
## [114,] 0.1024960403
## [115,] 0.4055375294
## [116,] 0.1215844890
## [117,] 0.0632073123
## [118,] 0.3716918801
## [119,] 0.0290522348
## [120,] 0.0440602930
## [121,] 0.0852624114
## [122,] 0.1853028469
## [123,] 0.2775002341
## [124,] 0.1367147056
## [125,] 0.2086384448
## [126,] 0.1291887711
## [127,] 0.0644109978
## [128,] 0.0795566643
## [129,] 0.3030818011
## [130,] 0.0398764185
## [131,] 0.1644377329
## [132,] 0.1981156274
## [133,] 0.0646139741
## [134,] 0.9401011222
## [135,] 1.0000000000
## [136,] 0.0414147578
## [137,] 0.1750410581
## [138,] 0.1016347782
## [139,] 0.0269094541
## [140,] 0.1875735987
## [141,] 0.2732892980
## [142,] 0.1289348349
## [143,] 0.8024587551
## [144,] 0.0487435862
## [145,] 0.2039551193
## [146,] 0.1323175669
## [147,] 0.0211522550
## [148,] 0.0041787451
## [149,] 0.2076841076
## [150,] 0.1274676534
## [151,] 0.2980349655
## [152,] 0.0824880378
## [153,] 0.2975829911
## [154,] 0.0185180215
## [155,] 0.2658120805
## [156,] 0.0200524203
## [157,] 0.2430006125
## [158,] 0.2254292293
## [159,] 0.1158546647
## [160,] 0.1541781755
## [161,] 0.1562096264
## [162,] 0.6597713043
## [163,] 0.1003198097
## [164,] 0.0522071151
## [165,] 0.0420181854
## [166,] 0.9733408129
## [167,] 0.0680675293
## [168,] 0.3965420567
## [169,] 0.0674195873
## [170,] 0.0775541674
## [171,] 0.1408679031
## [172,] 0.0297752342
## [173,] 0.0555821800
## [174,] 0.1099736506
## [175,] 0.0820545732
## [176,] 0.1195913552
## [177,] 0.0865758887
## [178,] 0.0958493055
## [179,] 0.2404092543
## [180,] 0.0652661924
## [181,] 0.1001090272
## [182,] 0.1613654523
## [183,] 0.0329451987
## [184,] 0.2310062518
## [185,] 0.2612151861
## [186,] 0.2034146916
## [187,] 0.0793023996
## [188,] 0.2788828158
## [189,] 0.2404321698
## [190,] 0.0282518677
## [191,] 0.0622096671
## [192,] 0.1484307755
## [193,] 0.0604248904
## [194,] 0.0981313011
## [195,] 0.0544656142
## [196,] 0.1024323874
## [197,] 0.0789171000
## [198,] 0.0929947570
## [199,] 0.0976297803
## [200,] 0.1055412566
## [201,] 0.0911209496
## [202,] 0.1250464561
## [203,] 0.0014023121
## [204,] 0.5661795816
## [205,] 0.0642284878
## [206,] 0.1869548623
## [207,] 0.3502977216
## [208,] 0.0855992265
## [209,] 0.0779423594
## [210,] 0.0701549347
## [211,] 0.1039208220
## [212,] 0.0820154292
## [213,] 0.2215762078
## [214,] 0.1063675052
## [215,] 0.1018418417
## [216,] 0.3265715584
## [217,] 0.0483286162
## [218,] 0.1650718251
## [219,] 0.0594521914
## [220,] 0.2699165450
## [221,] 0.2328568756
## [222,] 0.2271036059
## [223,] 0.3351054366
## [224,] 0.1091188712
## [225,] 0.0291393498
## [226,] 0.0448075181
## [227,] 0.0691180238
## [228,] 0.1776537776
## [229,] 0.1686297921
## [230,] 0.0268803784
## [231,] 0.0902559718
## [232,] 0.0972306848
## [233,] 0.2782619217
## [234,] 0.1340528029
## [235,] 0.1039951321
## [236,] 0.0820332765
## [237,] 0.1074899556
## [238,] 0.6643278925
## [239,] 0.2918345790
## [240,] 0.3745790218
## [241,] 0.0487051963
## [242,] 0.1302645287
## [243,] 0.1380332959
## [244,] 0.0509015161
## [245,] 0.0871773286
## [246,] 0.0644817587
## [247,] 0.1513548838
## [248,] 0.3389828980
## [249,] 0.6331649932
## [250,] 0.0340529265
## [251,] 0.1761484630
## [252,] 0.0905376017
## [253,] 0.1366287081
## [254,] 0.6959191029
## [255,] 0.0617423930
## [256,] 0.0852404815
## [257,] 0.0536896675
## [258,] 0.0722336994
## [259,] 0.1469351336
## [260,] 0.1117784943
## [261,] 0.5198815106
## [262,] 0.2987969866
## [263,] 0.1467381692
## [264,] 0.1008280812
## [265,] 0.0585849835
## [266,] 0.0932834074
## [267,] 0.0568080311
## [268,] 0.0348910352
## [269,] 0.0491162978
## [270,] 0.1244039712
## [271,] 0.7153155739
## [272,] 0.0409004040
## [273,] 0.0386238575
## [274,] 0.5569551085
## [275,] 0.0071721675
## [276,] 0.3258820265
## [277,] 0.1496770445
## [278,] 0.0736898133
## [279,] 0.1645195874
## [280,] 0.1751974880
## [281,] 0.0323891614
## [282,] 0.0379664069
## [283,] 0.3087165357
## [284,] 0.1598922755
## [285,] 0.2362674706
## [286,] 0.0842762805
## [287,] 0.4757241076
## [288,] 0.6523494250
## [289,] 0.0215386809
## [290,] 0.1910645938
## [291,] 0.1701123824
## [292,] 0.2677743282
## [293,] 0.3492019870
## [294,] 0.3773528262
## [295,] 0.0629820332
## [296,] 0.0723800727
## [297,] 0.1059010351
## [298,] 0.0961345800
## [299,] 0.0840363405
## [300,] 0.1594496830
## [301,] 0.1075376354
## [302,] 0.0359410892
## [303,] 0.0243048325
## [304,] 0.0706184598
## [305,] 0.5905681689
## [306,] 0.5948063310
## [307,] 0.0659422557
## [308,] 0.0899548924
## [309,] 0.1003152193
## [310,] 0.0592870542
## [311,] 0.0878941921
## [312,] 0.0898633497
## [313,] 0.1724447831
## [314,] 0.1270512320
## [315,] 0.0269855435
## [316,] 0.0998977757
## [317,] 0.3237909621
## [318,] 0.0921414909
## [319,] 0.0531716284
## [320,] 0.1292811757
## [321,] 0.1853303651
## [322,] 0.0305566305
## [323,] 0.0938378171
## [324,] 0.3782672513
## [325,] 0.2424513055
## [326,] 0.0591178664
## [327,] 0.4039626782
## [328,] 0.0490281317
## [329,] 0.2020214813
## [330,] 0.5660488201
## [331,] 0.1197972469
## [332,] 0.0793840836
## [333,] 0.0178948591
## [334,] 0.0607075326
## [335,] 0.0195022347
## [336,] 0.1486988514
## [337,] 0.1957640234
## [338,] 0.0770774395
## [339,] 0.0903154405
## [340,] 0.0395587606
## [341,] 0.4932341201
## [342,] 0.1306028539
## [343,] 0.3438161496
## [344,] 0.9730965482
## [345,] 0.0240684265
## [346,] 0.3177375169
## [347,] 0.1713345806
## [348,] 0.0697340811
## [349,] 0.2160151719
## [350,] 0.1834988211
## [351,] 0.0162682299
## [352,] 0.7128607483
## [353,] 0.0133294832
## [354,] 0.1278921201
## [355,] 0.1958967789
## [356,] 0.2511643928
## [357,] 0.0309942559
## [358,] 0.0624932355
## [359,] 0.0988319616
## [360,] 0.0454726736
## [361,] 0.1250617705
## [362,] 0.0849358208
## [363,] 0.0583894859
## [364,] 0.0606078076
## [365,] 0.0626942618
## [366,] 0.0634653702
## [367,] 0.4346639603
## [368,] 0.0240021135
## [369,] 0.2396872240
## [370,] 0.2054744802
## [371,] 0.4355282541
## [372,] 0.0365578726
## [373,] 0.0754660236
## [374,] 0.0001545975
## [375,] 0.0926171705
## [376,] 0.2063305143
## [377,] 0.0853331696
## wavelet.HHH_glrlm_GrayLevelVariance
## [1,] 0.02468694
## [2,] 0.05726171
## [3,] 0.23996061
## [4,] 0.02922991
## [5,] 0.30141340
## [6,] 0.40809157
## [7,] 0.02454652
## [8,] 0.24701740
## [9,] 0.09152007
## [10,] 0.10623060
## [11,] 0.11730584
## [12,] 0.23342079
## [13,] 0.02461725
## [14,] 0.02450099
## [15,] 0.25141310
## [16,] 0.05721113
## [17,] 0.17497677
## [18,] 0.04223871
## [19,] 0.04088063
## [20,] 0.11850739
## [21,] 0.14715846
## [22,] 0.11201310
## [23,] 0.03465512
## [24,] 0.02459370
## [25,] 0.05813341
## [26,] 0.06278176
## [27,] 0.02468688
## [28,] 0.13578782
## [29,] 0.02467090
## [30,] 0.02637288
## [31,] 0.08748578
## [32,] 0.04960938
## [33,] 0.06005636
## [34,] 0.02469418
## [35,] 0.04280845
## [36,] 0.06597159
## [37,] 0.03156127
## [38,] 0.02407894
## [39,] 0.02469459
## [40,] 0.04654602
## [41,] 0.26190616
## [42,] 0.43325114
## [43,] 1.00000000
## [44,] 0.06167621
## [45,] 0.02633537
## [46,] 0.05048094
## [47,] 0.07729080
## [48,] 0.02465412
## [49,] 0.02760316
## [50,] 0.30122152
## [51,] 0.02467738
## [52,] 0.03433208
## [53,] 0.02425518
## [54,] 0.22246884
## [55,] 0.02453015
## [56,] 0.03469185
## [57,] 0.02542433
## [58,] 0.02464260
## [59,] 0.03430632
## [60,] 0.03292174
## [61,] 0.03029059
## [62,] 0.08367574
## [63,] 0.02464266
## [64,] 0.02626433
## [65,] 0.02467380
## [66,] 0.09387975
## [67,] 0.04844399
## [68,] 0.02450659
## [69,] 0.05805305
## [70,] 0.02453602
## [71,] 0.02448214
## [72,] 0.66223726
## [73,] 0.02461689
## [74,] 0.02025775
## [75,] 0.28041197
## [76,] 0.19001398
## [77,] 0.02470040
## [78,] 0.02465808
## [79,] 0.02468564
## [80,] 0.04766595
## [81,] 0.09251405
## [82,] 0.03747441
## [83,] 0.02395895
## [84,] 0.06965722
## [85,] 0.02465494
## [86,] 0.02461057
## [87,] 0.02402662
## [88,] 0.03331616
## [89,] 0.25071353
## [90,] 0.02452779
## [91,] 0.07562009
## [92,] 0.02294728
## [93,] 0.08417211
## [94,] 0.01796772
## [95,] 0.02463439
## [96,] 0.02851589
## [97,] 0.02428338
## [98,] 0.10054781
## [99,] 0.06892825
## [100,] 0.02467199
## [101,] 0.02436072
## [102,] 0.02350727
## [103,] 0.05383493
## [104,] 0.04863554
## [105,] 0.02459627
## [106,] 0.13065449
## [107,] 0.48663550
## [108,] 0.09514049
## [109,] 0.02452319
## [110,] 0.08505771
## [111,] 0.02466123
## [112,] 0.03911738
## [113,] 0.07614361
## [114,] 0.03273084
## [115,] 0.02155392
## [116,] 0.02416899
## [117,] 0.04964055
## [118,] 0.02441321
## [119,] 0.03857806
## [120,] 0.05403889
## [121,] 0.03478146
## [122,] 0.02467847
## [123,] 0.02470092
## [124,] 0.02469033
## [125,] 0.02442667
## [126,] 0.04387619
## [127,] 0.12092326
## [128,] 0.02517711
## [129,] 0.02439898
## [130,] 0.09625255
## [131,] 0.08361643
## [132,] 0.05430653
## [133,] 0.06855354
## [134,] 0.02009160
## [135,] 0.02068197
## [136,] 0.15341813
## [137,] 0.36062962
## [138,] 0.04895486
## [139,] 0.06699377
## [140,] 0.02466744
## [141,] 0.24072659
## [142,] 0.04472323
## [143,] 0.18917293
## [144,] 0.02469092
## [145,] 0.04480907
## [146,] 0.02465017
## [147,] 0.09552121
## [148,] 0.15515308
## [149,] 0.02425399
## [150,] 0.02466535
## [151,] 0.02346018
## [152,] 0.19958634
## [153,] 0.22178921
## [154,] 0.05112871
## [155,] 0.07939283
## [156,] 0.12906063
## [157,] 0.02459592
## [158,] 0.02363932
## [159,] 0.03117921
## [160,] 0.04419529
## [161,] 0.06703596
## [162,] 0.02387348
## [163,] 0.11299509
## [164,] 0.03670322
## [165,] 0.38134882
## [166,] 0.02306726
## [167,] 0.04689960
## [168,] 0.01417657
## [169,] 0.07941873
## [170,] 0.21939989
## [171,] 0.09166283
## [172,] 0.09051654
## [173,] 0.04450533
## [174,] 0.03260286
## [175,] 0.02457009
## [176,] 0.03933183
## [177,] 0.20253945
## [178,] 0.10791152
## [179,] 0.33559141
## [180,] 0.02461381
## [181,] 0.09876658
## [182,] 0.02467394
## [183,] 0.14167347
## [184,] 0.03572281
## [185,] 0.02440106
## [186,] 0.11337852
## [187,] 0.09611363
## [188,] 0.08790437
## [189,] 0.05517296
## [190,] 0.07719246
## [191,] 0.04094415
## [192,] 0.02897961
## [193,] 0.04927140
## [194,] 0.08734113
## [195,] 0.36178096
## [196,] 0.09359772
## [197,] 0.11347321
## [198,] 0.09937007
## [199,] 0.02444195
## [200,] 0.11135280
## [201,] 0.10089188
## [202,] 0.05410372
## [203,] 0.87394623
## [204,] 0.02464988
## [205,] 0.10954830
## [206,] 0.04254230
## [207,] 0.02455310
## [208,] 0.02465625
## [209,] 0.03854092
## [210,] 0.02467703
## [211,] 0.02802207
## [212,] 0.02467663
## [213,] 0.06034090
## [214,] 0.04847284
## [215,] 0.02461416
## [216,] 0.02467871
## [217,] 0.02640874
## [218,] 0.02446424
## [219,] 0.11848945
## [220,] 0.02339372
## [221,] 0.02442948
## [222,] 0.02440279
## [223,] 0.02401177
## [224,] 0.02431409
## [225,] 0.03018775
## [226,] 0.02468789
## [227,] 0.04724038
## [228,] 0.02467436
## [229,] 0.02453088
## [230,] 0.02588085
## [231,] 0.02467955
## [232,] 0.02466145
## [233,] 0.02451338
## [234,] 0.02428632
## [235,] 0.02455598
## [236,] 0.02463461
## [237,] 0.02439509
## [238,] 0.02387799
## [239,] 0.02460674
## [240,] 0.02439440
## [241,] 0.03236858
## [242,] 0.02434681
## [243,] 0.02468758
## [244,] 0.02469303
## [245,] 0.02458157
## [246,] 0.02465849
## [247,] 0.02468172
## [248,] 0.41240024
## [249,] 0.00000000
## [250,] 0.02469753
## [251,] 0.02465120
## [252,] 0.02467634
## [253,] 0.03125430
## [254,] 0.01588359
## [255,] 0.02468989
## [256,] 0.02469061
## [257,] 0.03515304
## [258,] 0.02631930
## [259,] 0.03884851
## [260,] 0.02460570
## [261,] 0.02320625
## [262,] 0.02428442
## [263,] 0.02452580
## [264,] 0.02462306
## [265,] 0.11670109
## [266,] 0.02467783
## [267,] 0.04686417
## [268,] 0.06356436
## [269,] 0.09756612
## [270,] 0.02468897
## [271,] 0.02179800
## [272,] 0.02469719
## [273,] 0.02469903
## [274,] 0.02031698
## [275,] 0.22230200
## [276,] 0.02438310
## [277,] 0.02467268
## [278,] 0.02467149
## [279,] 0.02459793
## [280,] 0.07461515
## [281,] 0.16326668
## [282,] 0.02985934
## [283,] 0.02434797
## [284,] 0.02426176
## [285,] 0.02466891
## [286,] 0.03590781
## [287,] 0.02223386
## [288,] 0.02319988
## [289,] 0.02469327
## [290,] 0.02453589
## [291,] 0.02462800
## [292,] 0.02468936
## [293,] 0.02432956
## [294,] 0.02441234
## [295,] 0.02469861
## [296,] 0.02833627
## [297,] 0.02461538
## [298,] 0.02465212
## [299,] 0.02464007
## [300,] 0.02451660
## [301,] 0.02466063
## [302,] 0.07467578
## [303,] 0.02463617
## [304,] 0.02802796
## [305,] 0.02238607
## [306,] 0.02210015
## [307,] 0.02469334
## [308,] 0.02470097
## [309,] 0.03474196
## [310,] 0.02466108
## [311,] 0.02680818
## [312,] 0.02383085
## [313,] 0.02245525
## [314,] 0.02462920
## [315,] 0.02462318
## [316,] 0.02443973
## [317,] 0.02210543
## [318,] 0.02467446
## [319,] 0.02466052
## [320,] 0.02453314
## [321,] 0.02408152
## [322,] 0.02465277
## [323,] 0.02461469
## [324,] 0.02076923
## [325,] 0.02469992
## [326,] 0.02465928
## [327,] 0.02199040
## [328,] 0.02459439
## [329,] 0.06321965
## [330,] 0.01881625
## [331,] 0.02463982
## [332,] 0.02449962
## [333,] 0.02469164
## [334,] 0.02468289
## [335,] 0.02470151
## [336,] 0.02446162
## [337,] 0.02420549
## [338,] 0.02455290
## [339,] 0.02468915
## [340,] 0.02456703
## [341,] 0.02320070
## [342,] 0.02456474
## [343,] 0.02320600
## [344,] 0.01863351
## [345,] 0.03672754
## [346,] 0.02432076
## [347,] 0.02465199
## [348,] 0.02466480
## [349,] 0.05833148
## [350,] 0.02455073
## [351,] 0.02469641
## [352,] 0.01604683
## [353,] 0.02512948
## [354,] 0.02460670
## [355,] 0.09401091
## [356,] 0.02383138
## [357,] 0.02469463
## [358,] 0.02468336
## [359,] 0.02453896
## [360,] 0.70001441
## [361,] 0.02463209
## [362,] 0.02423546
## [363,] 0.04885048
## [364,] 0.04415984
## [365,] 0.06222922
## [366,] 0.02464018
## [367,] 0.02455571
## [368,] 0.02456034
## [369,] 0.12526663
## [370,] 0.02443852
## [371,] 0.01817090
## [372,] 0.02459529
## [373,] 0.02466651
## [374,] 0.26674165
## [375,] 0.02469524
## [376,] 0.02457740
## [377,] 0.02459896
## log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized
## [1,] 0.46837179
## [2,] 0.39561007
## [3,] 0.19722698
## [4,] 0.23115092
## [5,] 0.38820269
## [6,] 0.52772357
## [7,] 0.45215479
## [8,] 0.28487529
## [9,] 0.41646780
## [10,] 0.57599233
## [11,] 0.11453981
## [12,] 0.13441546
## [13,] 0.35522357
## [14,] 0.25668205
## [15,] 0.60608328
## [16,] 0.12823709
## [17,] 0.38227909
## [18,] 0.32032487
## [19,] 0.52297025
## [20,] 0.45743621
## [21,] 0.31155879
## [22,] 0.44834490
## [23,] 0.38899647
## [24,] 0.50743626
## [25,] 0.50408197
## [26,] 0.59593425
## [27,] 0.52908127
## [28,] 0.58691364
## [29,] 0.34998384
## [30,] 0.23536649
## [31,] 0.69444324
## [32,] 0.31971300
## [33,] 0.50779654
## [34,] 0.40226537
## [35,] 0.56072185
## [36,] 0.37170428
## [37,] 0.25237841
## [38,] 0.32315914
## [39,] 0.17893982
## [40,] 0.26234418
## [41,] 0.49759525
## [42,] 0.50374895
## [43,] 0.20689140
## [44,] 0.61472178
## [45,] 0.41914720
## [46,] 0.00000000
## [47,] 0.56897805
## [48,] 0.42377039
## [49,] 0.23500848
## [50,] 0.40264917
## [51,] 0.70184675
## [52,] 0.08021918
## [53,] 0.45062828
## [54,] 0.70390677
## [55,] 0.40005883
## [56,] 0.38179926
## [57,] 0.33325750
## [58,] 0.45584220
## [59,] 0.30253027
## [60,] 0.47220705
## [61,] 0.40192399
## [62,] 0.35767969
## [63,] 0.37339325
## [64,] 0.33626320
## [65,] 0.77770520
## [66,] 0.48864188
## [67,] 0.40289328
## [68,] 0.47385164
## [69,] 0.24517089
## [70,] 0.41058411
## [71,] 0.10551680
## [72,] 0.67903989
## [73,] 0.49728998
## [74,] 0.30552746
## [75,] 0.49999959
## [76,] 0.22558430
## [77,] 0.53588910
## [78,] 0.59547005
## [79,] 0.29246366
## [80,] 0.58882802
## [81,] 0.52115537
## [82,] 0.24976922
## [83,] 0.71713262
## [84,] 0.31247284
## [85,] 0.33752983
## [86,] 0.49997542
## [87,] 0.64526260
## [88,] 0.40187594
## [89,] 0.49973905
## [90,] 0.29513888
## [91,] 0.29652648
## [92,] 0.53921262
## [93,] 0.50178496
## [94,] 0.70400186
## [95,] 0.09647050
## [96,] 0.27215951
## [97,] 0.57516545
## [98,] 0.35835914
## [99,] 0.19247304
## [100,] 0.58975176
## [101,] 0.35329659
## [102,] 0.82977162
## [103,] 0.35147640
## [104,] 0.47130209
## [105,] 0.38204722
## [106,] 0.37771765
## [107,] 0.57970689
## [108,] 0.66057987
## [109,] 0.29874167
## [110,] 0.51212491
## [111,] 0.55992878
## [112,] 0.60781784
## [113,] 0.54728028
## [114,] 0.48869588
## [115,] 0.85038859
## [116,] 0.41984575
## [117,] 0.39734953
## [118,] 0.38298529
## [119,] 0.51346858
## [120,] 0.52014295
## [121,] 0.14390885
## [122,] 0.50822343
## [123,] 0.37164726
## [124,] 0.31927572
## [125,] 0.60164106
## [126,] 0.47743273
## [127,] 0.53211775
## [128,] 0.48692207
## [129,] 0.23864968
## [130,] 0.72041594
## [131,] 0.66525936
## [132,] 0.57669528
## [133,] 0.52095728
## [134,] 0.20665267
## [135,] 0.30756589
## [136,] 0.46201649
## [137,] 0.76992047
## [138,] 0.49303702
## [139,] 0.45492373
## [140,] 0.22119221
## [141,] 0.52364929
## [142,] 0.58780614
## [143,] 0.57048908
## [144,] 0.60243064
## [145,] 0.33454514
## [146,] 0.38649325
## [147,] 0.44246816
## [148,] 0.43145537
## [149,] 0.37815158
## [150,] 0.42492321
## [151,] 0.52389673
## [152,] 0.25600299
## [153,] 0.62032101
## [154,] 0.48031438
## [155,] 0.30028912
## [156,] 0.62172892
## [157,] 0.29033841
## [158,] 0.04586470
## [159,] 0.33044379
## [160,] 0.35554163
## [161,] 0.43645995
## [162,] 0.47834825
## [163,] 0.35239476
## [164,] 0.22032546
## [165,] 0.49444474
## [166,] 0.25609101
## [167,] 0.34821985
## [168,] 0.43707486
## [169,] 0.51639348
## [170,] 0.29932258
## [171,] 0.32655597
## [172,] 0.44183815
## [173,] 0.39876073
## [174,] 0.33628845
## [175,] 0.33613268
## [176,] 0.33022477
## [177,] 0.24665053
## [178,] 0.49400944
## [179,] 0.32045673
## [180,] 0.69229652
## [181,] 0.32390505
## [182,] 0.20608224
## [183,] 0.33895709
## [184,] 0.15984272
## [185,] 0.47095663
## [186,] 0.41656840
## [187,] 0.28736045
## [188,] 0.22725221
## [189,] 0.41811731
## [190,] 0.56680997
## [191,] 0.43965508
## [192,] 0.54144719
## [193,] 0.48899044
## [194,] 0.29573865
## [195,] 0.67394414
## [196,] 1.00000000
## [197,] 0.60679341
## [198,] 0.18357419
## [199,] 0.42210158
## [200,] 0.47908687
## [201,] 0.38514332
## [202,] 0.26722618
## [203,] 0.68864539
## [204,] 0.48796207
## [205,] 0.46054953
## [206,] 0.37429640
## [207,] 0.53509176
## [208,] 0.50264835
## [209,] 0.29877172
## [210,] 0.44145204
## [211,] 0.31980275
## [212,] 0.45900236
## [213,] 0.29577284
## [214,] 0.39650752
## [215,] 0.50415831
## [216,] 0.16050850
## [217,] 0.54435629
## [218,] 0.40694630
## [219,] 0.58105720
## [220,] 0.59811997
## [221,] 0.59164128
## [222,] 0.62947891
## [223,] 0.48057625
## [224,] 0.42470340
## [225,] 0.79149766
## [226,] 0.47100979
## [227,] 0.52521114
## [228,] 0.53518272
## [229,] 0.57670869
## [230,] 0.42770286
## [231,] 0.39403055
## [232,] 0.58159608
## [233,] 0.52590067
## [234,] 0.42455229
## [235,] 0.30514241
## [236,] 0.74419673
## [237,] 0.29126561
## [238,] 0.34386463
## [239,] 0.38431969
## [240,] 0.51767067
## [241,] 0.69857237
## [242,] 0.51384919
## [243,] 0.26785447
## [244,] 0.27629242
## [245,] 0.26923340
## [246,] 0.68490277
## [247,] 0.39799281
## [248,] 0.40046011
## [249,] 0.13255969
## [250,] 0.29771397
## [251,] 0.43575801
## [252,] 0.01551398
## [253,] 0.10670026
## [254,] 0.71507073
## [255,] 0.52340546
## [256,] 0.48572161
## [257,] 0.37005833
## [258,] 0.60888660
## [259,] 0.74630282
## [260,] 0.44331530
## [261,] 0.55142911
## [262,] 0.47502578
## [263,] 0.36298844
## [264,] 0.32272625
## [265,] 0.54227899
## [266,] 0.37607568
## [267,] 0.48882061
## [268,] 0.30704393
## [269,] 0.38459325
## [270,] 0.17504699
## [271,] 0.44812371
## [272,] 0.55197304
## [273,] 0.43307644
## [274,] 0.32970870
## [275,] 0.19287979
## [276,] 0.56335965
## [277,] 0.25761718
## [278,] 0.53519440
## [279,] 0.36677962
## [280,] 0.69443689
## [281,] 0.68861094
## [282,] 0.72505653
## [283,] 0.61711705
## [284,] 0.68358593
## [285,] 0.23036049
## [286,] 0.36859721
## [287,] 0.69758117
## [288,] 0.31650478
## [289,] 0.57033317
## [290,] 0.58198456
## [291,] 0.39659172
## [292,] 0.37036917
## [293,] 0.13409199
## [294,] 0.13245027
## [295,] 0.25882128
## [296,] 0.49550031
## [297,] 0.44345560
## [298,] 0.43265032
## [299,] 0.31599199
## [300,] 0.37228789
## [301,] 0.39207278
## [302,] 0.30927647
## [303,] 0.49346050
## [304,] 0.40380562
## [305,] 0.61033044
## [306,] 0.45499130
## [307,] 0.64576173
## [308,] 0.43207126
## [309,] 0.44244065
## [310,] 0.49582429
## [311,] 0.60597318
## [312,] 0.64025874
## [313,] 0.67898429
## [314,] 0.67861786
## [315,] 0.44438499
## [316,] 0.70241708
## [317,] 0.44090333
## [318,] 0.28622521
## [319,] 0.58562476
## [320,] 0.39174856
## [321,] 0.43635707
## [322,] 0.14703197
## [323,] 0.58475412
## [324,] 0.53041369
## [325,] 0.36344237
## [326,] 0.49786172
## [327,] 0.54035319
## [328,] 0.33165473
## [329,] 0.45101903
## [330,] 0.48276398
## [331,] 0.54870522
## [332,] 0.37386956
## [333,] 0.40994489
## [334,] 0.39542689
## [335,] 0.35626982
## [336,] 0.49601939
## [337,] 0.34589333
## [338,] 0.45424731
## [339,] 0.22862550
## [340,] 0.43813963
## [341,] 0.52410217
## [342,] 0.45013579
## [343,] 0.39947401
## [344,] 0.22558430
## [345,] 0.37900825
## [346,] 0.30438817
## [347,] 0.16369908
## [348,] 0.57122647
## [349,] 0.54265078
## [350,] 0.49276081
## [351,] 0.11422308
## [352,] 0.35497520
## [353,] 0.61205276
## [354,] 0.40317496
## [355,] 0.60461698
## [356,] 0.46584020
## [357,] 0.32896942
## [358,] 0.57266857
## [359,] 0.47411485
## [360,] 0.39242365
## [361,] 0.55374022
## [362,] 0.47095787
## [363,] 0.43282733
## [364,] 0.55708940
## [365,] 0.50285577
## [366,] 0.26524860
## [367,] 0.55603808
## [368,] 0.51205326
## [369,] 0.49495634
## [370,] 0.54204099
## [371,] 0.48455814
## [372,] 0.68394071
## [373,] 0.54215267
## [374,] 0.54013822
## [375,] 0.55286576
## [376,] 0.42524069
## [377,] 0.58868835
## wavelet.HHH_glcm_Imc1 original_glcm_Imc1
## [1,] 0.78557512 0.5974617
## [2,] 0.63623375 0.3836748
## [3,] 0.79765178 0.6353641
## [4,] 0.42624589 0.6287230
## [5,] 0.66218288 0.6201732
## [6,] 0.87928084 0.6061095
## [7,] 0.64707245 0.6063673
## [8,] 0.49305060 0.6442708
## [9,] 0.61109207 0.7169235
## [10,] 0.78429477 0.6427045
## [11,] 0.72252076 0.4974608
## [12,] 0.62197240 0.4672865
## [13,] 0.40077985 0.6259188
## [14,] 0.56950732 0.6765981
## [15,] 0.90005472 0.6580235
## [16,] 0.64355240 0.5634098
## [17,] 0.67483871 0.5851337
## [18,] 0.74206731 0.3421135
## [19,] 0.07029596 0.6870062
## [20,] 0.89730569 0.4833092
## [21,] 0.66432280 0.6564636
## [22,] 0.70550085 0.3327764
## [23,] 0.61498805 0.5769439
## [24,] 0.90757010 0.5691738
## [25,] 0.77058072 0.4867855
## [26,] 0.77768554 0.6306650
## [27,] 0.92456729 0.4852931
## [28,] 0.73628151 0.4511921
## [29,] 0.80532791 0.4510983
## [30,] 0.76259633 0.4711976
## [31,] 0.47634218 0.4969576
## [32,] 0.91555555 0.5449658
## [33,] 0.67107091 0.4851942
## [34,] 0.46567489 0.4403595
## [35,] 0.33706555 0.7571528
## [36,] 0.39992925 0.5452879
## [37,] 0.67734937 0.3504600
## [38,] 0.63554554 0.6468203
## [39,] 0.78929940 0.4110843
## [40,] 0.51056263 0.5723502
## [41,] 0.59046213 0.4015711
## [42,] 0.83339267 0.6888027
## [43,] 0.00000000 0.4669719
## [44,] 0.70820021 0.4821561
## [45,] 0.79091122 0.6052957
## [46,] 0.52283867 0.5346119
## [47,] 0.74445973 0.6059030
## [48,] 0.51701323 0.5076235
## [49,] 0.49209365 0.6854542
## [50,] 0.90354978 0.5785392
## [51,] 0.83924539 0.5130586
## [52,] 0.38965957 0.6000239
## [53,] 0.85073302 0.7376504
## [54,] 0.73796665 0.6768743
## [55,] 0.49616619 0.6876578
## [56,] 0.82351669 0.6180067
## [57,] 0.45764914 0.4393475
## [58,] 0.35860962 0.5623940
## [59,] 0.80825308 0.4942012
## [60,] 0.72918199 0.5701798
## [61,] 0.73299594 0.5009152
## [62,] 0.56512311 0.7116873
## [63,] 0.71060438 0.4811409
## [64,] 0.53352121 0.4536435
## [65,] 0.58715545 0.5005107
## [66,] 0.84678934 0.4514946
## [67,] 0.74826231 0.4586349
## [68,] 0.80851024 0.5151120
## [69,] 0.45334478 0.5468140
## [70,] 0.76906622 0.6744227
## [71,] 0.74069225 0.2612310
## [72,] 0.77955713 0.6089121
## [73,] 0.68047507 0.5495327
## [74,] 0.59610203 0.6241450
## [75,] 0.84277444 0.6480506
## [76,] 0.77180416 0.3059860
## [77,] 0.49114361 0.5213882
## [78,] 0.59989690 0.5811221
## [79,] 0.69447033 0.4127552
## [80,] 0.44283392 0.5777437
## [81,] 0.85636982 0.5306019
## [82,] 0.42079257 0.6328414
## [83,] 0.89633558 0.5498110
## [84,] 0.70886119 0.4527076
## [85,] 0.55240348 0.3823938
## [86,] 0.70932252 0.6455485
## [87,] 1.00000000 0.6168166
## [88,] 0.65870241 0.3958695
## [89,] 0.66946653 0.6094406
## [90,] 0.85904977 0.7322772
## [91,] 0.86313433 0.5310997
## [92,] 0.92653557 0.6048761
## [93,] 0.64640651 0.7103583
## [94,] 0.97251114 0.3794250
## [95,] 0.38106454 0.5492588
## [96,] 0.52605184 0.5912806
## [97,] 0.91354995 0.6341827
## [98,] 0.68734372 0.5944632
## [99,] 0.59738926 0.6061563
## [100,] 0.31497988 0.6508920
## [101,] 0.81384449 0.4855455
## [102,] 0.96555977 0.4599923
## [103,] 0.70475719 0.6953158
## [104,] 0.72338918 0.6357044
## [105,] 0.87817323 0.7706742
## [106,] 0.60300087 0.6152810
## [107,] 0.94618439 0.5572474
## [108,] 0.84205330 0.6202746
## [109,] 0.91691903 0.5859012
## [110,] 0.96695301 0.6897557
## [111,] 0.30753772 0.5190932
## [112,] 0.54889572 0.7635802
## [113,] 0.75359555 0.6696827
## [114,] 0.66521102 0.7966671
## [115,] 0.79364852 0.6244304
## [116,] 0.87080371 0.5688483
## [117,] 0.75269460 0.6189423
## [118,] 0.86270344 0.7184025
## [119,] 0.64948389 0.6648894
## [120,] 0.71972902 0.6451297
## [121,] 0.67174288 0.5830253
## [122,] 0.53564483 0.6453857
## [123,] 0.51402973 0.6785081
## [124,] 0.23461164 0.6771387
## [125,] 0.82187239 0.6059149
## [126,] 0.82225524 0.7122110
## [127,] 0.65663368 0.6916699
## [128,] 0.42357496 0.7616071
## [129,] 0.68815790 0.5182926
## [130,] 0.82811200 0.5780338
## [131,] 0.86105124 0.6912316
## [132,] 0.91665599 0.5885116
## [133,] 0.58607813 0.5478108
## [134,] 0.72673324 0.3082642
## [135,] 0.76210885 0.0000000
## [136,] 0.79940836 0.5256759
## [137,] 0.73588471 0.5716992
## [138,] 0.82336696 0.6943038
## [139,] 0.46420879 0.7047227
## [140,] 0.78938641 0.5894360
## [141,] 0.92159109 0.5846585
## [142,] 0.64610668 0.6477536
## [143,] 0.70739488 0.6815142
## [144,] 0.66084352 0.3988072
## [145,] 0.72852291 0.6698247
## [146,] 0.35066960 0.7408021
## [147,] 0.50785959 0.7233350
## [148,] 0.64270120 0.4418181
## [149,] 0.78556515 0.6495896
## [150,] 0.73209079 0.6296856
## [151,] 0.99204838 0.6684695
## [152,] 0.47528798 0.6163288
## [153,] 0.69306658 0.5427467
## [154,] 0.65844971 0.5039517
## [155,] 0.76410111 0.7256395
## [156,] 0.58929253 0.4446884
## [157,] 0.31493490 0.6536800
## [158,] 0.79583401 0.5922205
## [159,] 0.35535427 0.4131408
## [160,] 0.63120584 0.8424968
## [161,] 0.42440835 0.6417256
## [162,] 0.88865257 0.6883137
## [163,] 0.57462815 0.7011877
## [164,] 0.56032325 0.5700686
## [165,] 0.77179450 0.5138837
## [166,] 0.99611115 0.4713473
## [167,] 0.57259847 0.5801150
## [168,] 0.76196514 0.6025323
## [169,] 0.62146121 0.6891754
## [170,] 0.89584525 0.6230400
## [171,] 0.68080442 0.6910220
## [172,] 0.71376472 0.6651958
## [173,] 0.56435487 0.3171578
## [174,] 0.62148523 0.8167598
## [175,] 0.91950476 0.3810624
## [176,] 0.85036081 0.6327375
## [177,] 0.64465452 0.6271295
## [178,] 0.83625273 0.5684683
## [179,] 0.79627274 0.7545271
## [180,] 0.73930186 0.5854617
## [181,] 0.88566445 0.5090529
## [182,] 0.81596909 0.5742091
## [183,] 0.65227808 0.7643426
## [184,] 0.70089089 0.6362627
## [185,] 0.80485846 0.7210089
## [186,] 0.79122921 0.7961719
## [187,] 0.74094306 0.7132737
## [188,] 0.81520373 0.5951191
## [189,] 0.63634374 0.7456983
## [190,] 0.50957399 0.8382592
## [191,] 0.54803774 0.8577418
## [192,] 0.17984722 0.4574785
## [193,] 0.69790068 0.7866406
## [194,] 0.58904293 0.6945957
## [195,] 0.78207938 0.4186415
## [196,] 0.81715115 0.7767067
## [197,] 0.71261626 0.6772955
## [198,] 0.51377276 0.8072636
## [199,] 0.92062941 0.7078910
## [200,] 0.69295195 0.5897953
## [201,] 0.69238095 0.7459934
## [202,] 0.63430141 0.9337585
## [203,] 0.34791374 0.7742219
## [204,] 0.24438748 0.5081341
## [205,] 0.41840670 0.6005563
## [206,] 0.48948013 1.0000000
## [207,] 0.57261781 0.6740078
## [208,] 0.65718253 0.6178888
## [209,] 0.79902648 0.6042390
## [210,] 0.90415811 0.5515179
## [211,] 0.91111382 0.5506612
## [212,] 0.90883845 0.6989566
## [213,] 0.80627894 0.7112481
## [214,] 0.47611826 0.7471389
## [215,] 0.88587501 0.6760961
## [216,] 0.90672247 0.8029830
## [217,] 0.86188183 0.7386491
## [218,] 0.84635200 0.6555490
## [219,] 0.59431698 0.5000552
## [220,] 0.86782726 0.6557466
## [221,] 0.77020260 0.6839729
## [222,] 0.79350495 0.6963238
## [223,] 0.69453330 0.5735563
## [224,] 0.80288973 0.6075053
## [225,] 0.93756070 0.7142502
## [226,] 0.68414786 0.7000890
## [227,] 0.35012412 0.7027043
## [228,] 0.89898168 0.6423707
## [229,] 0.55979013 0.6621614
## [230,] 0.93268532 0.5993198
## [231,] 0.83628956 0.7715645
## [232,] 0.84286294 0.6897763
## [233,] 0.82855993 0.6249720
## [234,] 0.82381201 0.7153520
## [235,] 0.70120362 0.6801883
## [236,] 0.94543436 0.6445383
## [237,] 0.89938975 0.7275066
## [238,] 0.66696301 0.3698965
## [239,] 0.94060705 0.6413675
## [240,] 0.88414048 0.6490250
## [241,] 0.89085231 0.3457726
## [242,] 0.72355059 0.6455466
## [243,] 0.44823456 0.6131980
## [244,] 0.74090293 0.7345998
## [245,] 0.90318557 0.4659463
## [246,] 0.94039450 0.5939657
## [247,] 0.76641436 0.7034593
## [248,] 0.70998307 0.7963726
## [249,] 0.65070266 0.3836095
## [250,] 0.91901758 0.5814662
## [251,] 0.84813265 0.6461546
## [252,] 0.77030359 0.6527885
## [253,] 0.71764197 0.6641516
## [254,] 0.56672170 0.4903877
## [255,] 0.83678841 0.7452913
## [256,] 0.92123272 0.6528166
## [257,] 0.49861068 0.4811421
## [258,] 0.61084651 0.6315345
## [259,] 0.23896114 0.6154746
## [260,] 0.86771089 0.6207456
## [261,] 0.81249324 0.4332890
## [262,] 0.93758215 0.7260182
## [263,] 0.91726443 0.5135999
## [264,] 0.84230949 0.5867181
## [265,] 0.63066478 0.8246320
## [266,] 0.84851731 0.6060024
## [267,] 0.36284168 0.6863969
## [268,] 0.83352958 0.5218745
## [269,] 0.87722166 0.4100910
## [270,] 0.86917813 0.4082873
## [271,] 0.61807847 0.4820556
## [272,] 0.90666312 0.6884145
## [273,] 0.85207392 0.5442850
## [274,] 0.69652034 0.4319667
## [275,] 0.81100354 0.4595319
## [276,] 0.79903685 0.4807905
## [277,] 0.80936855 0.9123639
## [278,] 0.85827838 0.5947842
## [279,] 0.92477156 0.5023956
## [280,] 0.70093367 0.6918846
## [281,] 0.61432798 0.5051472
## [282,] 0.60033041 0.5654044
## [283,] 0.86452853 0.6674173
## [284,] 0.89049548 0.6596051
## [285,] 0.67692630 0.7006044
## [286,] 0.89515538 0.5732125
## [287,] 0.10457971 0.6425711
## [288,] 0.88359806 0.5650470
## [289,] 0.94622376 0.5429147
## [290,] 0.73743276 0.6711321
## [291,] 0.84698699 0.6881964
## [292,] 0.74105772 0.7160414
## [293,] 0.80567526 0.5941261
## [294,] 0.93735840 0.5435818
## [295,] 0.92418483 0.6761796
## [296,] 0.94984414 0.7463727
## [297,] 0.81672489 0.7208360
## [298,] 0.96992842 0.8562163
## [299,] 0.71999343 0.6602016
## [300,] 0.83971625 0.6252043
## [301,] 0.86479335 0.6592578
## [302,] 0.85346843 0.8156138
## [303,] 0.79888063 0.7360908
## [304,] 0.90522145 0.6493178
## [305,] 0.40698338 0.6856121
## [306,] 0.70927936 0.7302066
## [307,] 0.91243447 0.4748750
## [308,] 0.87928277 0.8440993
## [309,] 0.57866517 0.7210682
## [310,] 0.89077902 0.6490335
## [311,] 0.81697547 0.6509724
## [312,] 0.92530091 0.6376111
## [313,] 0.86653288 0.5079249
## [314,] 0.64964981 0.6115968
## [315,] 0.54118040 0.6674955
## [316,] 0.89943729 0.6094712
## [317,] 0.38066867 0.6255949
## [318,] 0.86009412 0.4135059
## [319,] 0.93725404 0.4029331
## [320,] 0.85915777 0.5435732
## [321,] 0.89545026 0.6363566
## [322,] 0.74805214 0.6459732
## [323,] 0.88947020 0.5477564
## [324,] 0.85300202 0.5137629
## [325,] 0.85518321 0.8308842
## [326,] 0.86461042 0.5732494
## [327,] 0.64387719 0.7030154
## [328,] 0.87625817 0.6504694
## [329,] 0.76241451 0.5667207
## [330,] 0.63997521 0.7008195
## [331,] 0.83362069 0.4974067
## [332,] 0.79000310 0.6439915
## [333,] 0.92033119 0.5794120
## [334,] 0.73600544 0.5602711
## [335,] 0.85492943 0.3223998
## [336,] 0.86217722 0.6027740
## [337,] 0.83020371 0.5880964
## [338,] 0.92550939 0.4798754
## [339,] 0.93139173 0.7710039
## [340,] 0.84039801 0.4419493
## [341,] 0.81895432 0.6602312
## [342,] 0.87061605 0.5387792
## [343,] 0.86282962 0.7442099
## [344,] 0.94223187 0.5699740
## [345,] 0.90064600 0.4819570
## [346,] 0.71332816 0.3842747
## [347,] 0.85539967 0.6631661
## [348,] 0.78866355 0.5526509
## [349,] 0.89056035 0.6701675
## [350,] 0.58511638 0.6689740
## [351,] 0.92815292 0.5720813
## [352,] 0.90791604 0.4587622
## [353,] 0.88288123 0.7101940
## [354,] 0.88981322 0.5413008
## [355,] 0.66332936 0.7533712
## [356,] 0.76934983 0.5919199
## [357,] 0.92763474 0.4865512
## [358,] 0.89629287 0.5903515
## [359,] 0.78018071 0.3790481
## [360,] 0.72472975 0.3043305
## [361,] 0.89914568 0.5861956
## [362,] 0.68643155 0.6621793
## [363,] 0.89455901 0.6616855
## [364,] 0.49218287 0.7091953
## [365,] 0.62863409 0.7850116
## [366,] 0.91764654 0.4925178
## [367,] 0.59808196 0.6368312
## [368,] 0.87760232 0.5596440
## [369,] 0.84477193 0.6091646
## [370,] 0.78368693 0.6851551
## [371,] 0.76079184 0.5139544
## [372,] 0.63104728 0.5217567
## [373,] 0.83477042 0.5586444
## [374,] 0.79426425 0.5207842
## [375,] 0.91971673 0.5313630
## [376,] 0.85758315 0.5458317
## [377,] 0.83851586 0.5709308
## log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis
## [1,] 0.370235046
## [2,] 0.582591689
## [3,] 0.238857645
## [4,] 0.325066310
## [5,] 0.654591627
## [6,] 0.325806187
## [7,] 0.140815992
## [8,] 0.251672375
## [9,] 0.334301171
## [10,] 0.251491225
## [11,] 0.198955556
## [12,] 0.472451611
## [13,] 0.214572004
## [14,] 0.423792102
## [15,] 0.047545056
## [16,] 0.435213739
## [17,] 0.474562129
## [18,] 0.467557383
## [19,] 0.530727399
## [20,] 0.392551917
## [21,] 0.187593002
## [22,] 0.378289644
## [23,] 0.388687634
## [24,] 0.421872811
## [25,] 0.412804224
## [26,] 0.275287906
## [27,] 0.552349071
## [28,] 1.000000000
## [29,] 0.498581949
## [30,] 0.378518260
## [31,] 0.357519591
## [32,] 0.191116874
## [33,] 0.468976328
## [34,] 0.457948400
## [35,] 0.355190982
## [36,] 0.550263241
## [37,] 0.461184749
## [38,] 0.212318611
## [39,] 0.464775914
## [40,] 0.298152531
## [41,] 0.365761098
## [42,] 0.143593401
## [43,] 0.228428567
## [44,] 0.473818344
## [45,] 0.237619547
## [46,] 0.755642033
## [47,] 0.325595108
## [48,] 0.456478690
## [49,] 0.354474544
## [50,] 0.313406494
## [51,] 0.431286790
## [52,] 0.574877889
## [53,] 0.070586864
## [54,] 0.054597437
## [55,] 0.205221933
## [56,] 0.271691619
## [57,] 0.542240204
## [58,] 0.311811980
## [59,] 0.218624619
## [60,] 0.282887697
## [61,] 0.201898442
## [62,] 0.401704354
## [63,] 0.350603632
## [64,] 0.354244878
## [65,] 0.372302771
## [66,] 0.189023812
## [67,] 0.337294257
## [68,] 0.077070963
## [69,] 0.278527889
## [70,] 0.042346424
## [71,] 0.171769829
## [72,] 0.176726115
## [73,] 0.293301026
## [74,] 0.063810693
## [75,] 0.234561355
## [76,] 0.149726871
## [77,] 0.573251153
## [78,] 0.456817331
## [79,] 0.464015175
## [80,] 0.931501750
## [81,] 0.477510798
## [82,] 0.567435083
## [83,] 0.082736945
## [84,] 0.413429173
## [85,] 0.387293009
## [86,] 0.071244212
## [87,] 0.140115371
## [88,] 0.262780414
## [89,] 0.758790702
## [90,] 0.074933482
## [91,] 0.318254796
## [92,] 0.066760205
## [93,] 0.540725100
## [94,] 0.115091654
## [95,] 0.227087911
## [96,] 0.300773837
## [97,] 0.073978461
## [98,] 0.534626746
## [99,] 0.605964419
## [100,] 0.447401140
## [101,] 0.190255073
## [102,] 0.193998655
## [103,] 0.538836350
## [104,] 0.369504235
## [105,] 0.162236973
## [106,] 0.378021487
## [107,] 0.370022789
## [108,] 0.267943831
## [109,] 0.212882048
## [110,] 0.175155710
## [111,] 0.290386932
## [112,] 0.644757850
## [113,] 0.202676250
## [114,] 0.464006965
## [115,] 0.042876471
## [116,] 0.200719177
## [117,] 0.823637391
## [118,] 0.105701277
## [119,] 0.626144091
## [120,] 0.760618753
## [121,] 0.330758613
## [122,] 0.454841192
## [123,] 0.515298023
## [124,] 0.279484987
## [125,] 0.130925853
## [126,] 0.331578566
## [127,] 0.509039537
## [128,] 0.546348857
## [129,] 0.066250946
## [130,] 0.436268575
## [131,] 0.160170302
## [132,] 0.077445969
## [133,] 0.560016237
## [134,] 0.044228610
## [135,] 0.260858216
## [136,] 0.703290239
## [137,] 0.209817047
## [138,] 0.209681202
## [139,] 0.539448847
## [140,] 0.071275628
## [141,] 0.113549460
## [142,] 0.320139975
## [143,] 0.062718796
## [144,] 0.278712032
## [145,] 0.242146269
## [146,] 0.182421633
## [147,] 0.681468440
## [148,] 0.647522805
## [149,] 0.158853834
## [150,] 0.176973301
## [151,] 0.129053196
## [152,] 0.411469815
## [153,] 0.165709739
## [154,] 0.639068273
## [155,] 0.170999953
## [156,] 0.841244988
## [157,] 0.195119351
## [158,] 0.065657623
## [159,] 0.187840607
## [160,] 0.453679701
## [161,] 0.176293553
## [162,] 0.031469847
## [163,] 0.371649032
## [164,] 0.493987634
## [165,] 0.389105395
## [166,] 0.206736670
## [167,] 0.289683978
## [168,] 0.112769925
## [169,] 0.435139629
## [170,] 0.230900130
## [171,] 0.285714605
## [172,] 0.446583522
## [173,] 0.581072065
## [174,] 0.392851296
## [175,] 0.134226602
## [176,] 0.269587211
## [177,] 0.163576700
## [178,] 0.408029837
## [179,] 0.096507784
## [180,] 0.428620864
## [181,] 0.263085731
## [182,] 0.206276717
## [183,] 0.829000305
## [184,] 0.243110624
## [185,] 0.168008702
## [186,] 0.156487054
## [187,] 0.477115259
## [188,] 0.062045375
## [189,] 0.135478809
## [190,] 0.599152218
## [191,] 0.486629090
## [192,] 0.351626669
## [193,] 0.422160428
## [194,] 0.572933220
## [195,] 0.338824225
## [196,] 0.239443561
## [197,] 0.489164408
## [198,] 0.340073622
## [199,] 0.219893807
## [200,] 0.383960138
## [201,] 0.406312247
## [202,] 0.488956833
## [203,] 0.697147641
## [204,] 0.058749859
## [205,] 0.428584763
## [206,] 0.491105540
## [207,] 0.070149303
## [208,] 0.270225323
## [209,] 0.217730445
## [210,] 0.248175854
## [211,] 0.376710486
## [212,] 0.433743785
## [213,] 0.035070468
## [214,] 0.273387815
## [215,] 0.202525660
## [216,] 0.330842938
## [217,] 0.488202627
## [218,] 0.214489897
## [219,] 0.310276414
## [220,] 0.116769242
## [221,] 0.108611986
## [222,] 0.067067592
## [223,] 0.182447250
## [224,] 0.167295349
## [225,] 0.597990886
## [226,] 0.546252642
## [227,] 0.356762620
## [228,] 0.362982411
## [229,] 0.125160405
## [230,] 0.599403162
## [231,] 0.496882929
## [232,] 0.302188363
## [233,] 0.180804789
## [234,] 0.175610412
## [235,] 0.306214364
## [236,] 0.255090621
## [237,] 0.392921100
## [238,] 0.134102989
## [239,] 0.253471905
## [240,] 0.238975016
## [241,] 0.518574702
## [242,] 0.306872845
## [243,] 0.264561093
## [244,] 0.427813994
## [245,] 0.272872738
## [246,] 0.337417427
## [247,] 0.266654432
## [248,] 0.084040974
## [249,] 0.080773656
## [250,] 0.627976659
## [251,] 0.214888682
## [252,] 0.276013932
## [253,] 0.152553153
## [254,] 0.021052467
## [255,] 0.497051020
## [256,] 0.598352563
## [257,] 0.423297021
## [258,] 0.553445978
## [259,] 0.201550494
## [260,] 0.217949120
## [261,] 0.155528298
## [262,] 0.251858629
## [263,] 0.262541628
## [264,] 0.160548373
## [265,] 0.538575502
## [266,] 0.273273533
## [267,] 0.457006672
## [268,] 0.304397602
## [269,] 0.574296964
## [270,] 0.497470086
## [271,] 0.064952197
## [272,] 0.497022823
## [273,] 0.598539490
## [274,] 0.162812734
## [275,] 0.846201630
## [276,] 0.160405347
## [277,] 0.333140821
## [278,] 0.462168723
## [279,] 0.288217144
## [280,] 0.194947276
## [281,] 0.408026345
## [282,] 0.383909956
## [283,] 0.157577359
## [284,] 0.196408543
## [285,] 0.370448168
## [286,] 0.513587072
## [287,] 0.058888339
## [288,] 0.143555406
## [289,] 0.514600787
## [290,] 0.223067295
## [291,] 0.286377210
## [292,] 0.510605265
## [293,] 0.076898871
## [294,] 0.125550600
## [295,] 0.351550866
## [296,] 0.498175182
## [297,] 0.205025298
## [298,] 0.268139739
## [299,] 0.396956057
## [300,] 0.101603846
## [301,] 0.208010042
## [302,] 0.732940688
## [303,] 0.540816704
## [304,] 0.278443516
## [305,] 0.114279453
## [306,] 0.072942090
## [307,] 0.532352683
## [308,] 0.476942799
## [309,] 0.162199018
## [310,] 0.331536663
## [311,] 0.341209687
## [312,] 0.259912826
## [313,] 0.185071387
## [314,] 0.223287613
## [315,] 0.424643654
## [316,] 0.298828263
## [317,] 0.087105717
## [318,] 0.347250045
## [319,] 0.519773696
## [320,] 0.220466971
## [321,] 0.155870153
## [322,] 0.524542058
## [323,] 0.284533810
## [324,] 0.077891686
## [325,] 0.505917412
## [326,] 0.346507194
## [327,] 0.116397172
## [328,] 0.202844732
## [329,] 0.123736504
## [330,] 0.167443810
## [331,] 0.363571799
## [332,] 0.247359919
## [333,] 0.527527988
## [334,] 0.512273498
## [335,] 0.595082805
## [336,] 0.311933102
## [337,] 0.093963273
## [338,] 0.530052944
## [339,] 0.364214875
## [340,] 0.281577555
## [341,] 0.034338072
## [342,] 0.307663887
## [343,] 0.000000000
## [344,] 0.005838047
## [345,] 0.505003586
## [346,] 0.074911195
## [347,] 0.151439342
## [348,] 0.298223465
## [349,] 0.125719482
## [350,] 0.175711402
## [351,] 0.422555533
## [352,] 0.157220984
## [353,] 0.700499215
## [354,] 0.230768949
## [355,] 0.255719555
## [356,] 0.159409787
## [357,] 0.518126836
## [358,] 0.407443386
## [359,] 0.275099306
## [360,] 0.326298637
## [361,] 0.294082515
## [362,] 0.229878848
## [363,] 0.269008202
## [364,] 0.202583976
## [365,] 0.527957439
## [366,] 0.421299802
## [367,] 0.087532872
## [368,] 0.435952127
## [369,] 0.064091109
## [370,] 0.095755167
## [371,] 0.129819861
## [372,] 0.477899677
## [373,] 0.605472845
## [374,] 0.946031314
## [375,] 0.448340451
## [376,] 0.190523824
## [377,] 0.267200342
## wavelet.LHH_glszm_GrayLevelNonUniformity wavelet.LHH_firstorder_Skewness
## [1,] 0.4655574840 0.4305169
## [2,] 0.6845680000 0.4567967
## [3,] 0.2329841242 0.4249651
## [4,] 0.3047577207 0.4444595
## [5,] 0.3482586456 0.6468989
## [6,] 0.5050233338 0.4560050
## [7,] 0.0591718902 0.4737787
## [8,] 0.2475202411 0.4312547
## [9,] 0.4559276663 0.4434438
## [10,] 0.1095651621 0.4281703
## [11,] 0.4329225950 0.4274876
## [12,] 0.7647444266 0.4315676
## [13,] 0.0238232402 0.4935751
## [14,] 0.0933374284 0.5374459
## [15,] 0.0672984524 0.4256607
## [16,] 0.4209223357 0.4121836
## [17,] 0.7164624944 0.4052953
## [18,] 0.3719351824 0.4107758
## [19,] 0.3416725704 0.3940125
## [20,] 0.5203838513 0.4380388
## [21,] 0.1526851746 0.4395243
## [22,] 0.4188045986 0.4768410
## [23,] 0.3589679262 0.4660426
## [24,] 0.1520854253 0.5470975
## [25,] 0.2751830008 0.4150633
## [26,] 0.2810371537 0.4616503
## [27,] 0.1811333908 0.5750146
## [28,] 0.4089934402 0.4885338
## [29,] 0.1952362679 0.4381250
## [30,] 0.2417865540 0.4345257
## [31,] 0.1518690833 0.5284220
## [32,] 0.0826140295 0.4548166
## [33,] 0.3958815597 0.4079613
## [34,] 0.1658250436 0.6465604
## [35,] 0.1850394641 0.4496364
## [36,] 0.2652056759 0.5033883
## [37,] 0.3689364816 0.4745196
## [38,] 0.0420200336 0.6031790
## [39,] 0.3435456010 0.4350595
## [40,] 0.3216638324 0.4484375
## [41,] 0.2710335366 0.4757540
## [42,] 0.0641402100 0.5584535
## [43,] 0.0273020343 0.6185072
## [44,] 0.2716163817 0.4949746
## [45,] 0.5160054761 0.3693158
## [46,] 0.1895701427 0.5391384
## [47,] 0.3173202571 0.4894825
## [48,] 0.0577488493 0.3663542
## [49,] 0.1416783866 0.4040435
## [50,] 0.7004730080 0.4259288
## [51,] 0.1300111697 0.5329849
## [52,] 0.3304335347 0.4180556
## [53,] 0.0739043543 0.4052917
## [54,] 0.0147981372 0.0000000
## [55,] 0.0606515643 0.5314826
## [56,] 0.1119549389 0.4421605
## [57,] 0.0718278907 0.5298095
## [58,] 0.0266296408 0.4767320
## [59,] 0.3718641094 0.3986965
## [60,] 0.1116065123 0.4621647
## [61,] 0.1467758470 0.4164603
## [62,] 0.4363580117 0.4764360
## [63,] 0.1437916165 0.4931451
## [64,] 0.1966021105 0.3348667
## [65,] 0.1375511238 0.8591261
## [66,] 0.0626925797 0.4586475
## [67,] 0.1930568881 0.4745891
## [68,] 0.0528125320 0.4370235
## [69,] 0.1265316244 0.4609399
## [70,] 0.0555273759 0.4093342
## [71,] 0.0170600012 0.4766933
## [72,] 0.1336162337 0.4740707
## [73,] 0.0487005516 0.4707740
## [74,] 0.0113874505 0.4522255
## [75,] 0.2024904853 0.4472276
## [76,] 0.0188603858 0.5287533
## [77,] 0.1598643605 0.6801276
## [78,] 0.1943787815 0.5584605
## [79,] 0.1480982677 0.5217028
## [80,] 0.1579281096 0.7689944
## [81,] 0.4925878491 0.4386760
## [82,] 0.4360063928 0.4430262
## [83,] 0.0248965826 0.5962288
## [84,] 0.3024816645 0.4407699
## [85,] 0.1768098806 0.4893431
## [86,] 0.0885440551 0.4496505
## [87,] 0.0796985389 0.4572145
## [88,] 0.1616135503 0.4704139
## [89,] 0.5234016016 0.5144088
## [90,] 0.0340136016 0.4636024
## [91,] 0.0407160129 0.5598580
## [92,] 0.0124260019 0.5987905
## [93,] 0.3265073429 0.4923271
## [94,] 0.0191720839 0.6278424
## [95,] 0.0055297987 0.2067180
## [96,] 0.2241035384 0.4795736
## [97,] 0.0393509906 0.3673519
## [98,] 0.6523157084 0.4161656
## [99,] 0.4993314139 0.4099802
## [100,] 0.0389701665 0.2757090
## [101,] 0.0675013493 0.4520173
## [102,] 0.0519061345 0.4691194
## [103,] 0.1653667958 0.3786115
## [104,] 0.1263200163 0.4637433
## [105,] 0.0927055280 0.3936661
## [106,] 0.3488155300 0.4642294
## [107,] 0.1601696244 0.4971547
## [108,] 0.3300161961 0.4445756
## [109,] 0.0287159929 0.5371245
## [110,] 0.1583255364 0.5066360
## [111,] 0.0339970202 0.5312775
## [112,] 0.2592928553 0.4876868
## [113,] 0.1140998660 0.5183741
## [114,] 0.1569554851 0.6124598
## [115,] 0.0321745524 0.5087597
## [116,] 0.0131643003 0.7994471
## [117,] 0.3916568067 0.4543472
## [118,] 0.0818951664 0.4796635
## [119,] 0.4852489853 0.5566760
## [120,] 0.3423367026 0.5153486
## [121,] 0.6208031175 0.4432144
## [122,] 0.0858214020 0.4890216
## [123,] 0.2102701054 0.4432944
## [124,] 0.1375401575 0.4991561
## [125,] 0.0588417970 0.5546966
## [126,] 0.1808877201 0.5062901
## [127,] 0.3783202371 0.3963506
## [128,] 0.1691858154 0.5656988
## [129,] 0.0279658806 0.4820682
## [130,] 0.2500394302 0.5098288
## [131,] 0.0943202268 0.4377860
## [132,] 0.0611025032 0.6165305
## [133,] 0.2375950335 0.4155150
## [134,] 0.0032961237 0.2749523
## [135,] 0.0244253643 0.4019418
## [136,] 0.2694547607 0.4457530
## [137,] 0.0623829498 0.5396228
## [138,] 0.1374338178 0.4680028
## [139,] 0.2200743973 0.4957881
## [140,] 0.1563799800 0.4406165
## [141,] 0.1113099112 0.4691029
## [142,] 0.1192693096 0.4932323
## [143,] 0.0261179607 0.5077576
## [144,] 0.1346195240 0.4844464
## [145,] 0.2279975595 0.4459696
## [146,] 0.1410639171 0.4357119
## [147,] 0.4908623394 0.4215328
## [148,] 0.3971167493 0.3931277
## [149,] 0.0971352052 0.4876005
## [150,] 0.1716640407 0.4306731
## [151,] 0.0622098905 0.5359122
## [152,] 0.2054113360 0.4678964
## [153,] 0.0305364242 0.3634059
## [154,] 0.5534967598 0.4303121
## [155,] 0.2123012178 0.4504607
## [156,] 0.4978800226 0.4482387
## [157,] 0.0071248034 0.5205280
## [158,] 0.0364190442 0.2828786
## [159,] 0.0544980189 0.4404600
## [160,] 0.4416015713 0.4450461
## [161,] 0.0876646077 0.4034636
## [162,] 0.0296170497 0.4144484
## [163,] 0.3548969390 0.4041732
## [164,] 0.4257866804 0.5027299
## [165,] 0.5467778439 0.4630287
## [166,] 0.0271524413 0.4015859
## [167,] 0.4481418059 0.4063064
## [168,] 0.0400934113 0.4483265
## [169,] 1.0000000000 0.4249308
## [170,] 0.3691581445 0.4004609
## [171,] 0.2306255016 0.4332853
## [172,] 0.2969736136 0.5030480
## [173,] 0.1053221414 0.6143583
## [174,] 0.4270727292 0.4383213
## [175,] 0.0102227706 0.4890017
## [176,] 0.1925812011 0.3925676
## [177,] 0.1384819840 0.4630251
## [178,] 0.4081402059 0.4676434
## [179,] 0.1757959008 0.4146830
## [180,] 0.1692727270 0.5330835
## [181,] 0.3221436355 0.4788144
## [182,] 0.1797498959 0.4363738
## [183,] 0.1685733157 0.4901450
## [184,] 0.3584757813 0.4542415
## [185,] 0.0852331383 0.5056518
## [186,] 0.2168516623 0.4366396
## [187,] 0.3230586855 0.4674468
## [188,] 0.0456850994 0.3933809
## [189,] 0.0799546194 0.4173954
## [190,] 0.2696238209 0.4978175
## [191,] 0.3906482045 0.4808703
## [192,] 0.0650697639 0.4798563
## [193,] 0.2961507894 0.4575035
## [194,] 0.8757875656 0.4579699
## [195,] 0.0787894046 0.4270595
## [196,] 0.1084200090 0.5148152
## [197,] 0.5077195365 0.4653124
## [198,] 0.4041852412 0.4429829
## [199,] 0.0393128947 0.5138625
## [200,] 0.1822483832 0.3805394
## [201,] 0.6486509842 0.4071370
## [202,] 0.7602731302 0.4373662
## [203,] 0.1479852859 0.7207810
## [204,] 0.0193307313 0.4672979
## [205,] 0.1950432791 0.5036602
## [206,] 0.6965382015 0.4368131
## [207,] 0.0830717413 0.4122266
## [208,] 0.1788047871 0.4377110
## [209,] 0.1016824987 0.3135710
## [210,] 0.3713332155 0.4253485
## [211,] 0.1714766118 0.4724085
## [212,] 0.1428606411 0.4629898
## [213,] 0.0735342324 0.3929179
## [214,] 0.1389393973 0.4143357
## [215,] 0.0742171599 0.5038305
## [216,] 0.0164813914 0.4726440
## [217,] 0.1654257861 0.4355219
## [218,] 0.1106359103 0.4217429
## [219,] 0.4429095432 0.3930178
## [220,] 0.0116101738 0.3917155
## [221,] 0.0571533853 0.4341936
## [222,] 0.0381422671 0.4139615
## [223,] 0.0049362994 0.4673669
## [224,] 0.0683572724 0.4515798
## [225,] 0.2016189830 0.6372669
## [226,] 0.2835682708 0.4749591
## [227,] 0.2233462559 0.4052311
## [228,] 0.1600312130 0.3610529
## [229,] 0.0820076649 0.4332191
## [230,] 0.3061695400 0.4394827
## [231,] 0.3209968286 0.4354498
## [232,] 0.1830499513 0.5371930
## [233,] 0.1561346359 0.3871029
## [234,] 0.1276374896 0.4353253
## [235,] 0.1146560916 0.4427614
## [236,] 0.0911393005 0.4587279
## [237,] 0.0619119904 0.5229215
## [238,] 0.0111448198 0.5010107
## [239,] 0.0877959145 0.4678461
## [240,] 0.0127964056 0.5392641
## [241,] 0.6453919560 0.4961439
## [242,] 0.1086841762 0.4691051
## [243,] 0.1016520386 0.4467989
## [244,] 0.3106817065 0.4042219
## [245,] 0.0583375169 0.2906679
## [246,] 0.0523414331 0.5885187
## [247,] 0.3482890892 0.4391362
## [248,] 0.0546977745 0.4922247
## [249,] 0.0000000000 0.4910802
## [250,] 0.2030671521 0.3977464
## [251,] 0.1775135680 0.4482308
## [252,] 0.2437067414 0.4297003
## [253,] 0.2761205158 0.3665650
## [254,] 0.0148755861 0.4642307
## [255,] 0.2702002318 0.4455352
## [256,] 0.1066783985 0.5522450
## [257,] 0.1573297207 0.4979747
## [258,] 0.3321643016 0.5303195
## [259,] 0.1872502220 0.4527563
## [260,] 0.1472184142 0.3767336
## [261,] 0.0031787679 0.4582816
## [262,] 0.0346807766 0.5135185
## [263,] 0.2088891787 0.4405338
## [264,] 0.1801147625 0.4515138
## [265,] 0.0760518834 0.6362271
## [266,] 0.2157459840 0.4062476
## [267,] 0.2716507662 0.4177443
## [268,] 0.2468731239 0.4897432
## [269,] 0.4785107911 0.4237950
## [270,] 0.3253345013 0.4485047
## [271,] 0.0177967308 0.3221444
## [272,] 0.3398746323 0.5484578
## [273,] 0.2157658652 0.4534456
## [274,] 0.0157802792 0.3446313
## [275,] 0.1706508923 0.7329233
## [276,] 0.0371897186 0.4955803
## [277,] 0.0479637313 0.3828750
## [278,] 0.2810996541 0.5467505
## [279,] 0.0609058335 0.4854759
## [280,] 0.1374764516 0.4544607
## [281,] 0.1830077737 0.5034336
## [282,] 0.4876489413 0.4796164
## [283,] 0.0601932988 0.4572966
## [284,] 0.1231320080 0.5613395
## [285,] 0.0170051609 0.4944731
## [286,] 0.2335554286 0.5012153
## [287,] 0.0204214902 0.4633876
## [288,] 0.0084334506 0.5376841
## [289,] 0.2178237253 0.6387789
## [290,] 0.0613634215 0.4798319
## [291,] 0.0750657611 0.4157389
## [292,] 0.0688264131 0.5258210
## [293,] 0.1037019422 0.4194183
## [294,] 0.0330319535 0.4519286
## [295,] 0.1752850026 0.4128149
## [296,] 0.1134085333 0.5388071
## [297,] 0.1788733006 0.4508912
## [298,] 0.1052487600 0.5185807
## [299,] 0.1387126679 0.4690284
## [300,] 0.0419502660 0.4470615
## [301,] 0.2271066451 0.4597068
## [302,] 0.0627103197 0.6534730
## [303,] 0.2351167678 0.5295706
## [304,] 0.2066001887 0.5119060
## [305,] 0.0554272426 0.4563739
## [306,] 0.0438296580 0.4404943
## [307,] 0.5541334434 0.4686544
## [308,] 0.2895729803 0.4378602
## [309,] 0.1375493627 0.4344326
## [310,] 0.1786093091 0.4685192
## [311,] 0.4169416344 0.4390478
## [312,] 0.0978410492 0.3784576
## [313,] 0.0510351073 0.4547404
## [314,] 0.1026035183 0.5315039
## [315,] 0.1815174314 0.5129727
## [316,] 0.2301749748 0.5114505
## [317,] 0.0380365155 0.4959548
## [318,] 0.3624957437 0.4422018
## [319,] 0.1570608799 0.5458407
## [320,] 0.0211797619 0.4051532
## [321,] 0.0403800447 0.4618535
## [322,] 0.0368400683 0.4056313
## [323,] 0.0445318754 0.6224676
## [324,] 0.0120800510 0.4933612
## [325,] 0.2022764704 0.4656574
## [326,] 0.3723417084 0.4858333
## [327,] 0.0229288863 0.4397346
## [328,] 0.0289313403 0.5188679
## [329,] 0.0285210771 0.5426516
## [330,] 0.0586615078 0.4723871
## [331,] 0.0578675865 0.5655732
## [332,] 0.1441843948 0.4995578
## [333,] 0.3963988234 0.5052430
## [334,] 0.1461646357 0.5470735
## [335,] 0.1786673598 0.6424788
## [336,] 0.0743358911 0.6022107
## [337,] 0.0626339520 0.4765038
## [338,] 0.0376824694 0.5579437
## [339,] 0.0115606881 0.4994700
## [340,] 0.0718100253 0.5135209
## [341,] 0.0447611729 0.4674091
## [342,] 0.0853478925 0.4402429
## [343,] 0.0122380140 0.4941332
## [344,] 0.0008495939 0.4118206
## [345,] 0.1989947963 0.5177713
## [346,] 0.0159820669 0.4378782
## [347,] 0.0714223357 0.4316822
## [348,] 0.1785399728 0.4706821
## [349,] 0.1145710126 0.4309420
## [350,] 0.2730089220 0.4668790
## [351,] 0.0971177416 0.3737167
## [352,] 0.0081719874 0.6576112
## [353,] 0.1866868716 0.4097584
## [354,] 0.1776010876 0.4679899
## [355,] 0.1678296446 0.4840453
## [356,] 0.0441454589 0.4462334
## [357,] 0.2014998811 0.4900565
## [358,] 0.2519814436 0.3571874
## [359,] 0.0330823746 0.4540768
## [360,] 0.0186036117 1.0000000
## [361,] 0.0975803847 0.4390314
## [362,] 0.0747705524 0.5054846
## [363,] 0.3711943001 0.3517641
## [364,] 0.1697511514 0.4668592
## [365,] 0.1847331631 0.5399103
## [366,] 0.0393870552 0.4879108
## [367,] 0.0481078416 0.4040932
## [368,] 0.1064629336 0.5822140
## [369,] 0.0591929427 0.5534573
## [370,] 0.0454997645 0.5336258
## [371,] 0.0130135850 0.5592399
## [372,] 0.1667794283 0.4744552
## [373,] 0.1400190773 0.4899175
## [374,] 0.0964808703 0.8325232
## [375,] 0.2719516267 0.4312134
## [376,] 0.0609732716 0.4064851
## [377,] 0.1576134252 0.5134057
## wavelet.HHH_glszm_GrayLevelNonUniformityNormalized wavelet.LHH_glcm_Imc1
## [1,] 0.85686313 0.5832767
## [2,] 0.36700480 0.7476941
## [3,] 0.42538457 0.9978089
## [4,] 0.21545885 0.8414534
## [5,] 0.29025003 0.7308959
## [6,] 0.45879800 0.6197845
## [7,] 0.61958669 0.5071728
## [8,] 0.37615869 0.8459888
## [9,] 0.33871067 0.8980362
## [10,] 0.13193907 0.6554794
## [11,] 0.34667364 0.9220752
## [12,] 0.47288565 1.0000000
## [13,] 0.61958669 0.7901611
## [14,] 0.61958669 0.3401653
## [15,] 0.29945036 0.6342589
## [16,] 0.26292959 0.7487000
## [17,] 0.45759705 0.7249314
## [18,] 0.23037151 0.7951284
## [19,] 0.16960196 0.8525069
## [20,] 0.25563840 0.6436503
## [21,] 0.35787020 0.8458168
## [22,] 0.22143525 0.7591594
## [23,] 0.06569688 0.8399380
## [24,] 0.61958669 0.7549597
## [25,] 0.04830552 0.7079584
## [26,] 0.15792149 0.6812290
## [27,] 0.51362024 0.6123936
## [28,] 0.21639091 0.6646882
## [29,] 1.00000000 0.8573130
## [30,] 0.36098572 0.8271387
## [31,] 0.19557456 0.6937505
## [32,] 0.27510657 0.5687196
## [33,] 0.23764279 0.7217009
## [34,] 0.66620421 0.8094621
## [35,] 0.14843801 0.8407649
## [36,] 0.30810562 0.8191197
## [37,] 0.26919144 0.8220759
## [38,] 0.55177097 0.8653988
## [39,] 0.61958669 0.7839893
## [40,] 0.10129663 0.8291092
## [41,] 0.42352959 0.7182841
## [42,] 0.15580341 0.7540862
## [43,] 0.26292959 0.1530488
## [44,] 0.14745780 0.8168268
## [45,] 0.24647663 0.7140177
## [46,] 0.17860596 0.6843259
## [47,] 0.33236111 0.7019664
## [48,] 0.75201048 0.9487556
## [49,] 0.27510657 0.7596252
## [50,] 0.47036751 0.6148115
## [51,] 0.55177097 0.6976577
## [52,] 0.20206749 0.8734336
## [53,] 0.61958669 0.5455185
## [54,] 0.22154571 0.7636139
## [55,] 0.51362024 0.8808711
## [56,] 0.19557456 0.6368909
## [57,] 0.38407258 0.8056939
## [58,] 0.61958669 0.8592321
## [59,] 0.09614025 0.7620096
## [60,] 0.19557456 0.7781710
## [61,] 0.19557456 0.8211275
## [62,] 0.19681736 0.7599800
## [63,] 0.61958669 0.9197350
## [64,] 0.37734183 0.7838789
## [65,] 0.53308529 0.7899417
## [66,] 0.08951053 0.5495149
## [67,] 0.19557456 0.8529675
## [68,] 0.61958669 0.7997527
## [69,] 0.15580341 0.7768946
## [70,] 0.51362024 0.8063612
## [71,] 0.61958669 0.9037547
## [72,] 0.38722337 0.6792797
## [73,] 0.51362024 0.7256681
## [74,] 0.51362024 0.2166152
## [75,] 0.28173362 0.6547008
## [76,] 0.27510657 0.3066936
## [77,] 0.75201048 0.8334010
## [78,] 0.51362024 0.8079429
## [79,] 0.61958669 0.7941426
## [80,] 0.07115787 0.8085687
## [81,] 0.26834162 0.7744196
## [82,] 0.30622405 0.7843020
## [83,] 0.51362024 0.6025038
## [84,] 0.27997543 0.8279764
## [85,] 0.53308529 0.8338880
## [86,] 0.75201048 0.7697320
## [87,] 0.61958669 0.7411978
## [88,] 0.09614025 0.8716792
## [89,] 0.40247645 0.5016487
## [90,] 0.75201048 0.7711561
## [91,] 0.08951053 0.8686545
## [92,] 0.61958669 0.6583075
## [93,] 0.33197944 0.6253687
## [94,] 0.51362024 0.5604185
## [95,] 0.51362024 0.9390585
## [96,] 0.27510657 0.8202142
## [97,] 0.61958669 0.5770973
## [98,] 0.41885677 0.6239462
## [99,] 0.28774258 0.6629403
## [100,] 0.61958669 0.8628914
## [101,] 0.51362024 0.4765800
## [102,] 0.55177097 0.6983283
## [103,] 0.06299070 0.6537906
## [104,] 0.09375356 0.6042149
## [105,] 0.55177097 0.7324736
## [106,] 0.27137881 0.7033164
## [107,] 0.44791961 0.3552858
## [108,] 0.19557456 0.7357678
## [109,] 0.51362024 0.3340847
## [110,] 0.03646935 0.4421002
## [111,] 0.51362024 0.9604200
## [112,] 0.06083496 0.7335320
## [113,] 0.14362788 0.6689613
## [114,] 0.27510657 0.6885451
## [115,] 0.61958669 0.6721292
## [116,] 0.55177097 0.7307303
## [117,] 0.20735795 0.8050627
## [118,] 0.51362024 0.6916698
## [119,] 0.06233317 0.7214706
## [120,] 0.30017989 0.7014007
## [121,] 0.06299070 0.7571776
## [122,] 0.53308529 0.8766712
## [123,] 0.85686313 0.9251246
## [124,] 0.75201048 0.7339384
## [125,] 0.51362024 0.4859525
## [126,] 0.19557456 0.7808047
## [127,] 0.41679007 0.6844862
## [128,] 0.36098572 0.8422320
## [129,] 0.61958669 0.6883125
## [130,] 0.32517523 0.5702448
## [131,] 0.03646935 0.5978146
## [132,] 0.19557456 0.5360325
## [133,] 0.12070837 0.5818181
## [134,] 0.51362024 0.6155850
## [135,] 0.61958669 0.0000000
## [136,] 0.26045915 0.6792687
## [137,] 0.23764279 0.3026481
## [138,] 0.27510657 0.7511710
## [139,] 0.19719780 0.6570419
## [140,] 0.61958669 0.7024092
## [141,] 0.27510657 0.6842271
## [142,] 0.08951053 0.8394250
## [143,] 0.19557456 0.5220347
## [144,] 0.51362024 0.8301436
## [145,] 0.09375356 0.5254203
## [146,] 0.61958669 0.7526194
## [147,] 0.18968276 0.5718962
## [148,] 0.36732072 0.6059104
## [149,] 0.61958669 0.4595013
## [150,] 0.61958669 0.7244524
## [151,] 0.51362024 0.6988194
## [152,] 0.31885708 0.7380721
## [153,] 0.09375356 0.4501262
## [154,] 0.24687193 0.7722411
## [155,] 0.08951053 0.7105349
## [156,] 0.22376070 0.5776892
## [157,] 0.53308529 0.8148094
## [158,] 0.55177097 0.6945869
## [159,] 0.19557456 0.7045809
## [160,] 0.40330273 0.8766229
## [161,] 0.06569688 0.7322237
## [162,] 0.51362024 0.8380500
## [163,] 0.37450227 0.7099703
## [164,] 0.05463985 0.7010171
## [165,] 0.40690064 0.5570061
## [166,] 0.51362024 0.6141262
## [167,] 0.28983321 0.8250551
## [168,] 0.61958669 0.7098773
## [169,] 0.34368324 0.7718095
## [170,] 0.37793155 0.6591302
## [171,] 0.29737307 0.8314393
## [172,] 0.31983729 0.6919174
## [173,] 0.18051370 0.8350556
## [174,] 0.07986713 0.8106618
## [175,] 0.55177097 0.6842859
## [176,] 0.08951053 0.6851462
## [177,] 0.20830061 0.7653082
## [178,] 0.23976071 0.5447022
## [179,] 0.23082590 0.6527845
## [180,] 0.61958669 0.7363362
## [181,] 0.36456367 0.6289087
## [182,] 0.75201048 0.8541184
## [183,] 0.40587729 0.7912476
## [184,] 0.09375356 0.7659038
## [185,] 0.61958669 0.8125679
## [186,] 0.09013820 0.6254673
## [187,] 0.37450227 0.6790849
## [188,] 0.27510657 0.7290054
## [189,] 0.19557456 0.8150880
## [190,] 0.32013564 0.7040896
## [191,] 0.19557456 0.6808179
## [192,] 0.37734183 0.8153673
## [193,] 0.14298388 0.6722064
## [194,] 0.35697725 0.6785256
## [195,] 0.31365875 0.4737127
## [196,] 0.06569688 0.7155620
## [197,] 0.31976572 0.7175889
## [198,] 0.30144454 0.7085560
## [199,] 0.51362024 0.8854565
## [200,] 0.35787020 0.5412313
## [201,] 0.41234063 0.7343044
## [202,] 0.25613885 0.8033655
## [203,] 0.02380464 0.6083311
## [204,] 0.51362024 0.5439054
## [205,] 0.22950990 0.7185035
## [206,] 0.39585114 0.6840466
## [207,] 0.51362024 0.6362275
## [208,] 0.55177097 0.7466014
## [209,] 0.10129663 0.7360377
## [210,] 0.85686313 0.6815368
## [211,] 0.30161419 0.7299836
## [212,] 0.57322937 0.7694605
## [213,] 0.09375356 0.7069024
## [214,] 0.14843801 0.8921476
## [215,] 0.68877611 0.6323423
## [216,] 0.51362024 0.8351629
## [217,] 0.39681265 0.6736230
## [218,] 0.61958669 0.4417834
## [219,] 0.35137947 0.7360139
## [220,] 0.61958669 0.5373750
## [221,] 0.51362024 0.4629988
## [222,] 0.61958669 0.6368517
## [223,] 0.75201048 0.5877510
## [224,] 0.61958669 0.5262245
## [225,] 0.43909798 0.6647475
## [226,] 0.57322937 0.7772691
## [227,] 0.09013820 0.6699195
## [228,] 0.61958669 0.6231906
## [229,] 0.51362024 0.5105263
## [230,] 0.63039822 0.7769538
## [231,] 0.61958669 0.7405003
## [232,] 0.61958669 0.7257590
## [233,] 0.68877611 0.6931409
## [234,] 0.61958669 0.5745603
## [235,] 0.68877611 0.6580441
## [236,] 0.55177097 0.6029566
## [237,] 0.61958669 0.7910242
## [238,] 0.61958669 0.5243238
## [239,] 0.61958669 0.7167083
## [240,] 0.51362024 0.8842956
## [241,] 0.12224895 0.4381424
## [242,] 0.61958669 0.6894805
## [243,] 0.61958669 0.7582291
## [244,] 0.51785936 0.6823043
## [245,] 0.51362024 0.5210288
## [246,] 0.61958669 0.8096992
## [247,] 0.51362024 0.6908737
## [248,] 0.19557456 0.5597654
## [249,] 0.61958669 0.7216048
## [250,] 0.67531013 0.7551052
## [251,] 0.55177097 0.5036505
## [252,] 0.61958669 0.8049963
## [253,] 0.30161419 0.7794683
## [254,] 0.85686313 0.7614406
## [255,] 0.52150288 0.7258605
## [256,] 1.00000000 0.8077249
## [257,] 0.27510657 0.6461997
## [258,] 0.24647663 0.7599240
## [259,] 0.08951053 0.2778170
## [260,] 0.53308529 0.5927650
## [261,] 0.75201048 0.8569276
## [262,] 0.61958669 0.6746383
## [263,] 0.51362024 0.6513558
## [264,] 0.53308529 0.5020558
## [265,] 0.31983729 0.8716814
## [266,] 0.61958669 0.6054596
## [267,] 0.10330023 0.8375935
## [268,] 0.13193907 0.6596534
## [269,] 0.18255999 0.6512708
## [270,] 0.52539549 0.6661795
## [271,] 0.61958669 0.2071488
## [272,] 0.56768780 0.7858130
## [273,] 0.59612217 0.6029087
## [274,] 0.61958669 0.2996716
## [275,] 0.18258844 0.6103016
## [276,] 0.51362024 0.6283305
## [277,] 0.61958669 0.8393221
## [278,] 0.61958669 0.7425754
## [279,] 0.75201048 0.6475165
## [280,] 0.14362788 0.5607454
## [281,] 0.18616158 0.6530373
## [282,] 0.65243334 0.5914579
## [283,] 0.51362024 0.4506867
## [284,] 0.93733987 0.6012245
## [285,] 0.61958669 0.8033941
## [286,] 0.14362788 0.6364981
## [287,] 0.51362024 0.3141022
## [288,] 0.55177097 0.2543295
## [289,] 0.56441193 0.8197027
## [290,] 0.51362024 0.4459641
## [291,] 0.61958669 0.6528034
## [292,] 0.51362024 0.8496623
## [293,] 0.75201048 0.6775282
## [294,] 0.85686313 0.6797144
## [295,] 0.61958669 0.7411387
## [296,] 0.37734183 0.8217598
## [297,] 0.61958669 0.5992649
## [298,] 0.51362024 0.6978610
## [299,] 0.85686313 0.6507772
## [300,] 0.85686313 0.6811496
## [301,] 0.53308529 0.7118634
## [302,] 0.03646935 0.7846781
## [303,] 0.53308529 0.8029378
## [304,] 0.30161419 0.5900274
## [305,] 0.51362024 0.3841794
## [306,] 0.61958669 0.5526904
## [307,] 0.51626236 0.6749369
## [308,] 0.51362024 0.8012283
## [309,] 0.19557456 0.6667584
## [310,] 0.54011414 0.7422419
## [311,] 0.38379379 0.5394659
## [312,] 0.61958669 0.5032136
## [313,] 0.61958669 0.6363649
## [314,] 0.85686313 0.3397318
## [315,] 0.58455915 0.7778079
## [316,] 0.52150288 0.4760809
## [317,] 0.55177097 0.4778233
## [318,] 0.85686313 0.5899703
## [319,] 0.54011414 0.8259862
## [320,] 0.75201048 0.6745699
## [321,] 0.61958669 0.6485527
## [322,] 0.53308529 0.6087112
## [323,] 0.55177097 0.4695635
## [324,] 0.55177097 0.5162972
## [325,] 0.51785936 0.8603356
## [326,] 0.51785936 0.5599391
## [327,] 0.51362024 0.4524537
## [328,] 0.55177097 0.6745822
## [329,] 0.19557456 0.6833346
## [330,] 0.51785936 0.2916605
## [331,] 0.93733987 0.6870339
## [332,] 0.61958669 0.6161644
## [333,] 0.56441193 0.7827404
## [334,] 0.53308529 0.7686581
## [335,] 0.75201048 0.8266899
## [336,] 0.55177097 0.6884821
## [337,] 0.55177097 0.4027749
## [338,] 0.51362024 0.7451151
## [339,] 0.61958669 0.8880001
## [340,] 0.75201048 0.5760652
## [341,] 0.61958669 0.4936428
## [342,] 0.51362024 0.7433218
## [343,] 0.51362024 0.5645611
## [344,] 0.61958669 0.8107533
## [345,] 0.04601721 0.6757247
## [346,] 0.51362024 0.5669914
## [347,] 0.85686313 0.6795876
## [348,] 0.75201048 0.5581257
## [349,] 0.19557456 0.7264220
## [350,] 0.55177097 0.7428777
## [351,] 0.51362024 0.8144867
## [352,] 0.51362024 0.6583123
## [353,] 0.56071945 0.8174681
## [354,] 0.61958669 0.6704422
## [355,] 0.06569688 0.6415412
## [356,] 0.53308529 0.5938245
## [357,] 0.56441193 0.8183949
## [358,] 0.66620421 0.6209203
## [359,] 0.55177097 0.4174898
## [360,] 0.17012142 0.1282501
## [361,] 0.51362024 0.6942865
## [362,] 0.68877611 0.6656560
## [363,] 0.21318837 0.6681236
## [364,] 0.29945036 0.7599807
## [365,] 0.48712482 0.8641602
## [366,] 0.55177097 0.7720022
## [367,] 0.61958669 0.6952481
## [368,] 0.61958669 0.6916846
## [369,] 0.09614025 0.5528411
## [370,] 0.51362024 0.6528929
## [371,] 0.51362024 0.6178882
## [372,] 0.55177097 0.4995130
## [373,] 0.51362024 0.8357350
## [374,] 0.00000000 0.4929665
## [375,] 0.75201048 0.7066188
## [376,] 0.75201048 0.6300711
## [377,] 0.52539549 0.6018482
## log.sigma.5.0.mm.3D_firstorder_Kurtosis
## [1,] 0.393540669
## [2,] 0.155461642
## [3,] 0.146273063
## [4,] 0.097344986
## [5,] 0.121957870
## [6,] 0.073281241
## [7,] 0.036878849
## [8,] 0.064822715
## [9,] 0.103859544
## [10,] 0.163383890
## [11,] 0.041366694
## [12,] 0.130004250
## [13,] 0.287178939
## [14,] 0.114554033
## [15,] 0.026851667
## [16,] 0.207444967
## [17,] 0.212667611
## [18,] 0.117391779
## [19,] 0.116606626
## [20,] 0.053915068
## [21,] 0.030380974
## [22,] 0.094725770
## [23,] 0.078687858
## [24,] 0.041433312
## [25,] 0.224997800
## [26,] 0.139763562
## [27,] 0.074556661
## [28,] 0.256906873
## [29,] 0.057291050
## [30,] 0.150047411
## [31,] 0.220528980
## [32,] 0.027699239
## [33,] 0.118741683
## [34,] 0.149373165
## [35,] 0.060393059
## [36,] 0.121198374
## [37,] 0.157153220
## [38,] 0.092939304
## [39,] 0.184941175
## [40,] 0.073593303
## [41,] 0.099257901
## [42,] 0.038464617
## [43,] 0.000000000
## [44,] 0.028227870
## [45,] 0.148133502
## [46,] 0.095920424
## [47,] 0.083885920
## [48,] 0.066027895
## [49,] 0.096997525
## [50,] 0.077171200
## [51,] 0.108584819
## [52,] 0.243495857
## [53,] 0.027316015
## [54,] 0.033642579
## [55,] 0.029185812
## [56,] 0.065134292
## [57,] 0.324782987
## [58,] 0.021137556
## [59,] 0.086639245
## [60,] 0.085869714
## [61,] 0.179544039
## [62,] 0.110058728
## [63,] 0.102943912
## [64,] 0.134993855
## [65,] 0.098475957
## [66,] 0.055032565
## [67,] 0.267458685
## [68,] 0.007119271
## [69,] 0.253345291
## [70,] 0.025289273
## [71,] 0.109239130
## [72,] 0.022283779
## [73,] 0.043929441
## [74,] 0.066649048
## [75,] 0.060341205
## [76,] 0.045708386
## [77,] 1.000000000
## [78,] 0.057680892
## [79,] 0.059675989
## [80,] 0.333998719
## [81,] 0.081752480
## [82,] 0.442501243
## [83,] 0.021475282
## [84,] 0.092460934
## [85,] 0.119265093
## [86,] 0.018296005
## [87,] 0.039401992
## [88,] 0.049166711
## [89,] 0.170332138
## [90,] 0.031229245
## [91,] 0.059516104
## [92,] 0.031276899
## [93,] 0.056039434
## [94,] 0.040029682
## [95,] 0.184718286
## [96,] 0.083310232
## [97,] 0.044560380
## [98,] 0.183114944
## [99,] 0.235424122
## [100,] 0.148168231
## [101,] 0.026176520
## [102,] 0.042480581
## [103,] 0.353317555
## [104,] 0.098471970
## [105,] 0.015815042
## [106,] 0.075369125
## [107,] 0.101006900
## [108,] 0.088683808
## [109,] 0.006258440
## [110,] 0.019042577
## [111,] 0.034923343
## [112,] 0.103343632
## [113,] 0.034542327
## [114,] 0.081427060
## [115,] 0.033135417
## [116,] 0.049383034
## [117,] 0.147899237
## [118,] 0.033000192
## [119,] 0.113369213
## [120,] 0.113753193
## [121,] 0.263350835
## [122,] 0.097246557
## [123,] 0.104580499
## [124,] 0.064157909
## [125,] 0.035060531
## [126,] 0.142625787
## [127,] 0.134925295
## [128,] 0.192932791
## [129,] 0.034681989
## [130,] 0.336871019
## [131,] 0.016644047
## [132,] 0.018826289
## [133,] 0.108254382
## [134,] 0.040643219
## [135,] 0.003685822
## [136,] 0.046394650
## [137,] 0.024623171
## [138,] 0.044391872
## [139,] 0.503086225
## [140,] 0.043685973
## [141,] 0.022552222
## [142,] 0.063804725
## [143,] 0.030883530
## [144,] 0.306174398
## [145,] 0.041005491
## [146,] 0.074142235
## [147,] 0.077170371
## [148,] 0.223064725
## [149,] 0.010415128
## [150,] 0.106275972
## [151,] 0.026155418
## [152,] 0.205704223
## [153,] 0.151668331
## [154,] 0.195766551
## [155,] 0.046123146
## [156,] 0.200692728
## [157,] 0.085730793
## [158,] 0.103078753
## [159,] 0.035401137
## [160,] 0.151234001
## [161,] 0.106521298
## [162,] 0.038825024
## [163,] 0.127220113
## [164,] 0.142149886
## [165,] 0.274956778
## [166,] 0.034156352
## [167,] 0.092235851
## [168,] 0.004344980
## [169,] 0.447914191
## [170,] 0.186259715
## [171,] 0.089367996
## [172,] 0.086619451
## [173,] 0.176347246
## [174,] 0.100830328
## [175,] 0.062327047
## [176,] 0.074541037
## [177,] 0.031655941
## [178,] 0.092829933
## [179,] 0.059038068
## [180,] 0.058989187
## [181,] 0.084246568
## [182,] 0.087476842
## [183,] 0.060906557
## [184,] 0.066292056
## [185,] 0.033108417
## [186,] 0.059507736
## [187,] 0.075681343
## [188,] 0.015427701
## [189,] 0.102498581
## [190,] 0.198326050
## [191,] 0.106112006
## [192,] 0.049727589
## [193,] 0.050937244
## [194,] 0.104960773
## [195,] 0.108145065
## [196,] 0.090289127
## [197,] 0.272149550
## [198,] 0.047556938
## [199,] 0.033083873
## [200,] 0.202571168
## [201,] 0.316076328
## [202,] 0.215683332
## [203,] 0.414734739
## [204,] 0.026858477
## [205,] 0.085192229
## [206,] 0.770567668
## [207,] 0.018715100
## [208,] 0.097085914
## [209,] 0.062891416
## [210,] 0.332182229
## [211,] 0.103147774
## [212,] 0.191936866
## [213,] 0.099219992
## [214,] 0.056595491
## [215,] 0.042640045
## [216,] 0.087611618
## [217,] 0.060629888
## [218,] 0.015054703
## [219,] 0.271765616
## [220,] 0.041630364
## [221,] 0.052078106
## [222,] 0.036931462
## [223,] 0.381606307
## [224,] 0.017180809
## [225,] 0.038612748
## [226,] 0.091679305
## [227,] 0.109650563
## [228,] 0.071554791
## [229,] 0.028043614
## [230,] 0.136380499
## [231,] 0.093980157
## [232,] 0.099999118
## [233,] 0.037443690
## [234,] 0.035575594
## [235,] 0.056548855
## [236,] 0.205526688
## [237,] 0.041393801
## [238,] 0.026899784
## [239,] 0.067799559
## [240,] 0.034130735
## [241,] 0.112306748
## [242,] 0.116813392
## [243,] 0.118678626
## [244,] 0.196945565
## [245,] 0.077448651
## [246,] 0.048900250
## [247,] 0.057694205
## [248,] 0.027315511
## [249,] 0.036457754
## [250,] 0.167930647
## [251,] 0.059730438
## [252,] 0.198590673
## [253,] 0.194065621
## [254,] 0.062831636
## [255,] 0.061399373
## [256,] 0.117811496
## [257,] 0.071592683
## [258,] 0.036561069
## [259,] 0.044237650
## [260,] 0.043438659
## [261,] 0.014567086
## [262,] 0.161256398
## [263,] 0.031563091
## [264,] 0.034681039
## [265,] 0.025383010
## [266,] 0.042712936
## [267,] 0.558339300
## [268,] 0.087741223
## [269,] 0.150098117
## [270,] 0.147326028
## [271,] 0.054026998
## [272,] 0.072527665
## [273,] 0.113994355
## [274,] 0.043780543
## [275,] 0.178303356
## [276,] 0.080131589
## [277,] 0.096108912
## [278,] 0.080347050
## [279,] 0.068728099
## [280,] 0.075989110
## [281,] 0.119395849
## [282,] 0.087745477
## [283,] 0.030891017
## [284,] 0.065713990
## [285,] 0.207728112
## [286,] 0.251898104
## [287,] 0.028386817
## [288,] 0.063818938
## [289,] 0.062535912
## [290,] 0.016796580
## [291,] 0.180452582
## [292,] 0.174829077
## [293,] 0.026258355
## [294,] 0.072578141
## [295,] 0.100590163
## [296,] 0.061198418
## [297,] 0.074803218
## [298,] 0.080066205
## [299,] 0.049245586
## [300,] 0.034759622
## [301,] 0.082339663
## [302,] 0.068567209
## [303,] 0.045868073
## [304,] 0.077787340
## [305,] 0.060355778
## [306,] 0.042808447
## [307,] 0.225755805
## [308,] 0.196422975
## [309,] 0.036820396
## [310,] 0.113252469
## [311,] 0.176335106
## [312,] 0.059254671
## [313,] 0.047328513
## [314,] 0.050462402
## [315,] 0.048194472
## [316,] 0.067737332
## [317,] 0.039478707
## [318,] 0.068492718
## [319,] 0.059239190
## [320,] 0.045161337
## [321,] 0.052002157
## [322,] 0.124278114
## [323,] 0.062667491
## [324,] 0.042402892
## [325,] 0.297220018
## [326,] 0.211404509
## [327,] 0.036148517
## [328,] 0.048567882
## [329,] 0.052064606
## [330,] 0.055008263
## [331,] 0.067857485
## [332,] 0.088276233
## [333,] 0.129217099
## [334,] 0.057312281
## [335,] 0.227191298
## [336,] 0.082548196
## [337,] 0.011031149
## [338,] 0.087637684
## [339,] 0.393125935
## [340,] 0.086740601
## [341,] 0.030960758
## [342,] 0.060515959
## [343,] 0.026220056
## [344,] 0.024403553
## [345,] 0.069375590
## [346,] 0.026022077
## [347,] 0.029450549
## [348,] 0.098474750
## [349,] 0.038250760
## [350,] 0.057614064
## [351,] 0.085326892
## [352,] 0.043118759
## [353,] 0.243790197
## [354,] 0.102238254
## [355,] 0.045866363
## [356,] 0.047394106
## [357,] 0.167648935
## [358,] 0.088043788
## [359,] 0.080713888
## [360,] 0.061276165
## [361,] 0.051053813
## [362,] 0.058080186
## [363,] 0.158121193
## [364,] 0.047889766
## [365,] 0.047914047
## [366,] 0.191328719
## [367,] 0.018192698
## [368,] 0.030941178
## [369,] 0.066504234
## [370,] 0.030250318
## [371,] 0.026746391
## [372,] 0.082780063
## [373,] 0.113482938
## [374,] 0.225052168
## [375,] 0.081968823
## [376,] 0.113081142
## [377,] 0.216715595
## log.sigma.5.0.mm.3D_firstorder_90Percentile
## [1,] 0.8679390
## [2,] 0.8981411
## [3,] 0.8778035
## [4,] 0.8750455
## [5,] 0.8667499
## [6,] 0.9023500
## [7,] 0.7531417
## [8,] 0.8799615
## [9,] 0.8481671
## [10,] 0.8923905
## [11,] 0.8996369
## [12,] 0.9035841
## [13,] 0.9029142
## [14,] 0.9133029
## [15,] 0.6271108
## [16,] 0.8960534
## [17,] 0.8942946
## [18,] 0.8942581
## [19,] 0.8570952
## [20,] 0.9204460
## [21,] 0.8191252
## [22,] 0.9126551
## [23,] 0.8552505
## [24,] 0.8352355
## [25,] 0.8932970
## [26,] 0.8671089
## [27,] 0.8482239
## [28,] 0.8830224
## [29,] 0.8652347
## [30,] 0.8803825
## [31,] 0.9189840
## [32,] 0.8547390
## [33,] 0.9023941
## [34,] 0.8646036
## [35,] 0.8072873
## [36,] 0.8628335
## [37,] 0.8798457
## [38,] 0.8249340
## [39,] 0.8853480
## [40,] 0.8582537
## [41,] 0.8911039
## [42,] 0.7278365
## [43,] 0.8472091
## [44,] 0.8446948
## [45,] 0.9058714
## [46,] 0.8738962
## [47,] 0.8461763
## [48,] 0.8865552
## [49,] 0.8743576
## [50,] 0.9140255
## [51,] 0.8655258
## [52,] 0.8581965
## [53,] 0.7731209
## [54,] 0.8382127
## [55,] 0.8534690
## [56,] 0.7271082
## [57,] 0.8639296
## [58,] 0.8502666
## [59,] 0.9220018
## [60,] 0.8246963
## [61,] 0.8576633
## [62,] 0.8582474
## [63,] 0.8593571
## [64,] 0.8761856
## [65,] 0.8775520
## [66,] 0.9253268
## [67,] 0.8856306
## [68,] 0.8026548
## [69,] 0.8962146
## [70,] 0.7128215
## [71,] 0.9332034
## [72,] 0.8436798
## [73,] 0.8436717
## [74,] 0.7669395
## [75,] 0.8582011
## [76,] 0.8111559
## [77,] 0.8621582
## [78,] 0.8577192
## [79,] 0.8666084
## [80,] 0.8531389
## [81,] 0.8874198
## [82,] 0.8754373
## [83,] 0.8553328
## [84,] 0.8724590
## [85,] 0.8544630
## [86,] 0.8541300
## [87,] 0.7679996
## [88,] 0.8863315
## [89,] 0.8745954
## [90,] 0.8718788
## [91,] 0.8648852
## [92,] 0.7047663
## [93,] 0.8698295
## [94,] 0.5743750
## [95,] 0.8451615
## [96,] 0.8430425
## [97,] 0.7923375
## [98,] 0.8858283
## [99,] 0.8814255
## [100,] 0.8682873
## [101,] 0.8412771
## [102,] 0.9873080
## [103,] 0.8472423
## [104,] 0.8301589
## [105,] 0.8595104
## [106,] 0.8655480
## [107,] 0.8848380
## [108,] 0.8657533
## [109,] 0.9479358
## [110,] 0.8271545
## [111,] 0.8589853
## [112,] 0.8567420
## [113,] 0.7897612
## [114,] 0.8418994
## [115,] 0.7045100
## [116,] 0.8605238
## [117,] 0.8594308
## [118,] 0.8461575
## [119,] 0.8552423
## [120,] 0.8638672
## [121,] 0.8805070
## [122,] 0.8374657
## [123,] 0.8552724
## [124,] 0.8342758
## [125,] 0.8647056
## [126,] 0.8367975
## [127,] 0.8736104
## [128,] 0.8563557
## [129,] 0.8913412
## [130,] 0.7803047
## [131,] 0.7724637
## [132,] 0.7095199
## [133,] 0.8457260
## [134,] 0.9305048
## [135,] 0.9871832
## [136,] 0.8990139
## [137,] 0.7419969
## [138,] 0.8280159
## [139,] 0.8830241
## [140,] 0.8824857
## [141,] 0.8373948
## [142,] 0.8657838
## [143,] 0.0000000
## [144,] 0.8631920
## [145,] 0.8650461
## [146,] 0.8608688
## [147,] 0.8863188
## [148,] 0.8655829
## [149,] 0.7840687
## [150,] 0.7879731
## [151,] 0.7767289
## [152,] 0.8323749
## [153,] 0.8741099
## [154,] 0.8730121
## [155,] 0.8443784
## [156,] 0.8975038
## [157,] 0.7977390
## [158,] 0.9066344
## [159,] 0.9004535
## [160,] 0.8589346
## [161,] 0.8027323
## [162,] 0.7043706
## [163,] 0.8721428
## [164,] 0.8795211
## [165,] 0.9089139
## [166,] 0.9270526
## [167,] 0.9000275
## [168,] 0.9140944
## [169,] 0.8862414
## [170,] 0.8807158
## [171,] 0.8549599
## [172,] 0.8694650
## [173,] 0.8696617
## [174,] 0.8547203
## [175,] 0.9040032
## [176,] 0.8195211
## [177,] 0.8796407
## [178,] 0.9639844
## [179,] 0.8361897
## [180,] 0.8568155
## [181,] 0.9100257
## [182,] 0.8798269
## [183,] 0.8541089
## [184,] 0.9141235
## [185,] 0.8600940
## [186,] 0.8022783
## [187,] 0.8443484
## [188,] 0.8705243
## [189,] 0.7172640
## [190,] 0.8568343
## [191,] 0.8517422
## [192,] 0.8637487
## [193,] 0.8362928
## [194,] 0.8793089
## [195,] 1.0000000
## [196,] 0.7605159
## [197,] 0.8688065
## [198,] 0.8646139
## [199,] 0.8839196
## [200,] 0.8971650
## [201,] 0.8896378
## [202,] 0.8573088
## [203,] 0.8570940
## [204,] 0.8302418
## [205,] 0.8646817
## [206,] 0.8552219
## [207,] 0.7524797
## [208,] 0.8471593
## [209,] 0.8854141
## [210,] 0.8757951
## [211,] 0.8724196
## [212,] 0.8602220
## [213,] 0.8678102
## [214,] 0.8598721
## [215,] 0.8502557
## [216,] 0.8401560
## [217,] 0.8459078
## [218,] 0.7912978
## [219,] 0.9343786
## [220,] 0.8166117
## [221,] 0.8320172
## [222,] 0.6936245
## [223,] 0.8393167
## [224,] 0.8108411
## [225,] 0.8662801
## [226,] 0.8611573
## [227,] 0.8354201
## [228,] 0.8801532
## [229,] 0.7727565
## [230,] 0.8602986
## [231,] 0.8731969
## [232,] 0.8134707
## [233,] 0.8879608
## [234,] 0.7993551
## [235,] 0.8674041
## [236,] 0.8299334
## [237,] 0.8263695
## [238,] 0.8259177
## [239,] 0.8637476
## [240,] 0.8030091
## [241,] 0.9646902
## [242,] 0.8312062
## [243,] 0.8718565
## [244,] 0.8733867
## [245,] 0.8919022
## [246,] 0.8309226
## [247,] 0.8735378
## [248,] 0.8583542
## [249,] 0.8130312
## [250,] 0.8732444
## [251,] 0.8752445
## [252,] 0.8739622
## [253,] 0.8893038
## [254,] 0.8200618
## [255,] 0.8657924
## [256,] 0.8521632
## [257,] 0.8744805
## [258,] 0.8521374
## [259,] 0.8563189
## [260,] 0.8665890
## [261,] 0.9565946
## [262,] 0.7542875
## [263,] 0.8616055
## [264,] 0.8359677
## [265,] 0.8592565
## [266,] 0.8921333
## [267,] 0.8524595
## [268,] 0.8713640
## [269,] 0.9419149
## [270,] 0.9125434
## [271,] 0.8186589
## [272,] 0.8502693
## [273,] 0.8800730
## [274,] 0.8996906
## [275,] 0.8875622
## [276,] 0.8630839
## [277,] 0.8527951
## [278,] 0.8547483
## [279,] 0.8611721
## [280,] 0.8281147
## [281,] 0.9122408
## [282,] 0.9059526
## [283,] 0.6814260
## [284,] 0.7620950
## [285,] 0.8416927
## [286,] 0.8655430
## [287,] 0.4498103
## [288,] 0.8979487
## [289,] 0.8497645
## [290,] 0.6372069
## [291,] 0.8384631
## [292,] 0.8576667
## [293,] 0.8818980
## [294,] 0.9087820
## [295,] 0.8683925
## [296,] 0.8395140
## [297,] 0.8151965
## [298,] 0.8554991
## [299,] 0.8598274
## [300,] 0.8185364
## [301,] 0.8461441
## [302,] 0.8528233
## [303,] 0.8479885
## [304,] 0.8205680
## [305,] 0.8072645
## [306,] 0.8061140
## [307,] 0.9245574
## [308,] 0.8535863
## [309,] 0.8441376
## [310,] 0.8361030
## [311,] 0.8405156
## [312,] 0.9164577
## [313,] 0.8575556
## [314,] 0.6313974
## [315,] 0.8690423
## [316,] 0.7726467
## [317,] 0.8348822
## [318,] 0.8794509
## [319,] 0.8481143
## [320,] 0.8259137
## [321,] 0.8409470
## [322,] 0.8649523
## [323,] 0.7739260
## [324,] 0.8379718
## [325,] 0.8547234
## [326,] 0.8594368
## [327,] 0.8544824
## [328,] 0.8602017
## [329,] 0.8858419
## [330,] 0.7687460
## [331,] 0.8517750
## [332,] 0.8197624
## [333,] 0.8643532
## [334,] 0.8500439
## [335,] 0.8577968
## [336,] 0.7868832
## [337,] 0.7746727
## [338,] 0.8237615
## [339,] 0.8724825
## [340,] 0.8557299
## [341,] 0.7660043
## [342,] 0.8390543
## [343,] 0.8836618
## [344,] 0.8856764
## [345,] 0.8511544
## [346,] 0.9393708
## [347,] 0.8302583
## [348,] 0.8013259
## [349,] 0.7665373
## [350,] 0.8444625
## [351,] 0.8499556
## [352,] 0.8032133
## [353,] 0.8728155
## [354,] 0.8487303
## [355,] 0.8563788
## [356,] 0.8043038
## [357,] 0.8533227
## [358,] 0.8389906
## [359,] 0.8493420
## [360,] 0.9358061
## [361,] 0.8162174
## [362,] 0.7793604
## [363,] 0.8937035
## [364,] 0.8713586
## [365,] 0.8453913
## [366,] 0.8524324
## [367,] 0.8687486
## [368,] 0.8326340
## [369,] 0.7863757
## [370,] 0.7217050
## [371,] 0.8821542
## [372,] 0.8207322
## [373,] 0.8520091
## [374,] 0.8559361
## [375,] 0.8420189
## [376,] 0.8307628
## [377,] 0.8411684
## wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis
## [1,] 0.077426784
## [2,] 0.102373302
## [3,] 0.099832641
## [4,] 0.103937468
## [5,] 0.034414893
## [6,] 0.016393901
## [7,] 0.213005235
## [8,] 0.124844585
## [9,] 0.051028183
## [10,] 0.131705713
## [11,] 0.059697274
## [12,] 0.049426477
## [13,] 0.765061990
## [14,] 0.150939969
## [15,] 0.144096915
## [16,] 0.096758212
## [17,] 0.114048557
## [18,] 0.096820110
## [19,] 0.048825616
## [20,] 0.092537546
## [21,] 0.270141573
## [22,] 0.099464070
## [23,] 0.094264253
## [24,] 0.248924734
## [25,] 0.092536802
## [26,] 0.095564492
## [27,] 0.087869574
## [28,] 0.027127734
## [29,] 0.051434198
## [30,] 0.086226368
## [31,] 0.115262672
## [32,] 0.103545884
## [33,] 0.089614231
## [34,] 0.211750109
## [35,] 0.274612188
## [36,] 0.104585275
## [37,] 0.100443427
## [38,] 0.177840956
## [39,] 0.216900527
## [40,] 0.238310220
## [41,] 0.055928661
## [42,] 0.319798285
## [43,] 0.515887960
## [44,] 0.113246527
## [45,] 0.227444924
## [46,] 0.055544790
## [47,] 0.045847482
## [48,] 0.093520378
## [49,] 0.216790020
## [50,] 0.025183192
## [51,] 0.209546501
## [52,] 0.103787813
## [53,] 0.212981742
## [54,] 0.268140389
## [55,] 0.103668312
## [56,] 0.235205306
## [57,] 0.186199230
## [58,] 0.258597506
## [59,] 0.085894449
## [60,] 0.229955597
## [61,] 0.274743281
## [62,] 0.088825957
## [63,] 0.191484181
## [64,] 0.109024659
## [65,] 0.161461812
## [66,] 0.253679683
## [67,] 0.055068484
## [68,] 0.682322844
## [69,] 0.233114506
## [70,] 0.224142981
## [71,] 0.742687740
## [72,] 0.101854410
## [73,] 0.229853051
## [74,] 0.756369042
## [75,] 0.086349740
## [76,] 0.238763740
## [77,] 0.107609582
## [78,] 0.110758336
## [79,] 0.127041646
## [80,] 0.018435083
## [81,] 0.094818627
## [82,] 0.103136888
## [83,] 0.618046044
## [84,] 0.095745731
## [85,] 0.193097507
## [86,] 0.089755979
## [87,] 0.289209865
## [88,] 0.077582699
## [89,] 0.060607600
## [90,] 0.278270142
## [91,] 0.073310681
## [92,] 0.270913472
## [93,] 0.113841952
## [94,] 0.160049819
## [95,] 0.124617280
## [96,] 0.108290683
## [97,] 0.192301023
## [98,] 0.105187939
## [99,] 0.090840304
## [100,] 0.854915680
## [101,] 0.578356720
## [102,] 0.254609672
## [103,] 0.134498662
## [104,] 0.096775205
## [105,] 0.223202971
## [106,] 0.104463979
## [107,] 0.009925312
## [108,] 0.099598488
## [109,] 0.256521271
## [110,] 0.095159308
## [111,] 0.222001000
## [112,] 0.053151298
## [113,] 0.258582910
## [114,] 0.209755844
## [115,] 0.238917356
## [116,] 0.156363195
## [117,] 0.051499393
## [118,] 0.100181875
## [119,] 0.087893001
## [120,] 0.110192578
## [121,] 0.105930256
## [122,] 0.749624983
## [123,] 0.226356495
## [124,] 0.211004893
## [125,] 0.608041335
## [126,] 0.110398578
## [127,] 0.125382288
## [128,] 0.224106320
## [129,] 0.268245551
## [130,] 0.109008413
## [131,] 0.122199010
## [132,] 0.324566866
## [133,] 0.112679328
## [134,] 0.976860908
## [135,] 0.195093317
## [136,] 0.026076198
## [137,] 0.134702116
## [138,] 0.112010669
## [139,] 0.092687105
## [140,] 0.211205686
## [141,] 0.140932935
## [142,] 0.228064441
## [143,] 0.366292569
## [144,] 0.183939181
## [145,] 0.085657202
## [146,] 0.265888372
## [147,] 0.095036041
## [148,] 0.046850637
## [149,] 0.210679505
## [150,] 0.251152254
## [151,] 0.156406275
## [152,] 0.089335685
## [153,] 0.193631867
## [154,] 0.050056298
## [155,] 0.260604107
## [156,] 0.000000000
## [157,] 0.267431234
## [158,] 0.284313498
## [159,] 0.754574216
## [160,] 0.117607963
## [161,] 0.324904236
## [162,] 0.312548184
## [163,] 0.101098273
## [164,] 0.095466166
## [165,] 0.054906379
## [166,] 0.114126173
## [167,] 0.236613542
## [168,] 0.145827248
## [169,] 0.100298649
## [170,] 0.057153246
## [171,] 0.199330604
## [172,] 0.112914370
## [173,] 0.105098867
## [174,] 0.113766892
## [175,] 0.858906522
## [176,] 0.206187705
## [177,] 0.238243131
## [178,] 0.123288296
## [179,] 0.260019568
## [180,] 0.252894670
## [181,] 0.048392386
## [182,] 0.176079536
## [183,] 0.034360704
## [184,] 0.112405496
## [185,] 0.733688879
## [186,] 0.101059259
## [187,] 0.098205476
## [188,] 0.447063548
## [189,] 0.152679378
## [190,] 0.115076679
## [191,] 0.236942769
## [192,] 0.190781433
## [193,] 0.092792667
## [194,] 0.104561616
## [195,] 0.136303600
## [196,] 0.107420133
## [197,] 0.050916279
## [198,] 0.269999847
## [199,] 0.719913931
## [200,] 0.126890417
## [201,] 0.102385772
## [202,] 0.239163134
## [203,] 0.012235149
## [204,] 0.999462895
## [205,] 0.056275924
## [206,] 0.335455815
## [207,] 0.209919632
## [208,] 0.171004391
## [209,] 0.230892624
## [210,] 0.086641236
## [211,] 0.093964870
## [212,] 0.213943588
## [213,] 0.225922955
## [214,] 0.282944063
## [215,] 0.161801144
## [216,] 0.435394188
## [217,] 0.105572371
## [218,] 0.104910147
## [219,] 0.030343308
## [220,] 1.000000000
## [221,] 0.735921361
## [222,] 0.194869517
## [223,] 0.547163514
## [224,] 0.179994503
## [225,] 0.182813707
## [226,] 0.223278067
## [227,] 0.257228966
## [228,] 0.278111671
## [229,] 0.215099573
## [230,] 0.042190239
## [231,] 0.172942033
## [232,] 0.248178267
## [233,] 0.088249068
## [234,] 0.088245217
## [235,] 0.580896398
## [236,] 0.155054575
## [237,] 0.202121705
## [238,] 0.224107461
## [239,] 0.203881918
## [240,] 0.212746089
## [241,] 0.085979191
## [242,] 0.094029623
## [243,] 0.583766129
## [244,] 0.208143261
## [245,] 0.224985803
## [246,] 0.111918992
## [247,] 0.223534754
## [248,] 0.181411144
## [249,] 0.273628515
## [250,] 0.086489774
## [251,] 0.205213330
## [252,] 0.129505676
## [253,] 0.082229804
## [254,] 0.223702051
## [255,] 0.175905466
## [256,] 0.209908788
## [257,] 0.223900572
## [258,] 0.080354740
## [259,] 0.084834515
## [260,] 0.086350820
## [261,] 0.339470575
## [262,] 0.210005093
## [263,] 0.178130342
## [264,] 0.184928092
## [265,] 0.102114780
## [266,] 0.158801118
## [267,] 0.104540837
## [268,] 0.080914243
## [269,] 0.104071263
## [270,] 0.215545162
## [271,] 0.251030827
## [272,] 0.208095717
## [273,] 0.174847008
## [274,] 0.496032142
## [275,] 0.023640088
## [276,] 0.372800654
## [277,] 0.181244900
## [278,] 0.195923673
## [279,] 0.198575735
## [280,] 0.103095788
## [281,] 0.061373267
## [282,] 0.101243178
## [283,] 0.159580145
## [284,] 0.208006974
## [285,] 0.440107471
## [286,] 0.181094357
## [287,] 0.530531255
## [288,] 0.673623721
## [289,] 0.045181342
## [290,] 0.176903546
## [291,] 0.096302778
## [292,] 0.653504361
## [293,] 0.798138290
## [294,] 0.190751952
## [295,] 0.245210199
## [296,] 0.060753874
## [297,] 0.130380399
## [298,] 0.195819269
## [299,] 0.189839961
## [300,] 0.704967424
## [301,] 0.188065652
## [302,] 0.039990445
## [303,] 0.196886281
## [304,] 0.217983502
## [305,] 0.206792950
## [306,] 0.158814493
## [307,] 0.081597482
## [308,] 0.236006495
## [309,] 0.266057136
## [310,] 0.109229412
## [311,] 0.100331979
## [312,] 0.143204519
## [313,] 0.478897224
## [314,] 0.498151423
## [315,] 0.210275169
## [316,] 0.207791027
## [317,] 0.152092219
## [318,] 0.202123687
## [319,] 0.186081535
## [320,] 0.237845580
## [321,] 0.191533298
## [322,] 0.229051940
## [323,] 0.081513148
## [324,] 0.491497961
## [325,] 0.770820656
## [326,] 0.087872713
## [327,] 0.599469727
## [328,] 0.561364201
## [329,] 0.136778412
## [330,] 0.152024728
## [331,] 0.129940372
## [332,] 0.185283670
## [333,] 0.216509821
## [334,] 0.205196506
## [335,] 0.165937884
## [336,] 0.595492299
## [337,] 0.415196191
## [338,] 0.129400977
## [339,] 0.092341927
## [340,] 0.676926586
## [341,] 0.329000623
## [342,] 0.209376752
## [343,] 0.781116159
## [344,] 0.683205566
## [345,] 0.113256595
## [346,] 0.502904251
## [347,] 0.221324180
## [348,] 0.186636987
## [349,] 0.275576532
## [350,] 0.122910210
## [351,] 0.249160924
## [352,] 0.224849091
## [353,] 0.203933452
## [354,] 0.100631829
## [355,] 0.102396491
## [356,] 0.247167005
## [357,] 0.118516457
## [358,] 0.158043195
## [359,] 0.218451128
## [360,] 0.060356401
## [361,] 0.101279906
## [362,] 0.190041756
## [363,] 0.105909341
## [364,] 0.233055020
## [365,] 0.282231814
## [366,] 0.173361243
## [367,] 0.222889679
## [368,] 0.213528470
## [369,] 0.234107048
## [370,] 0.182400198
## [371,] 0.404949575
## [372,] 0.161471560
## [373,] 0.214890651
## [374,] 0.010961175
## [375,] 0.183840744
## [376,] 0.211163408
## [377,] 0.162045983
## wavelet.HLH_glcm_DifferenceAverage
## [1,] 0.16978248
## [2,] 0.29593040
## [3,] 0.51321698
## [4,] 0.27812104
## [5,] 0.35868822
## [6,] 0.66071757
## [7,] 0.07090332
## [8,] 0.43496479
## [9,] 0.35602459
## [10,] 0.46972003
## [11,] 0.41796376
## [12,] 0.41546941
## [13,] 0.27981719
## [14,] 0.30970221
## [15,] 0.61914171
## [16,] 0.29611498
## [17,] 0.38238065
## [18,] 0.30637259
## [19,] 0.35269416
## [20,] 0.35809071
## [21,] 0.33154153
## [22,] 0.26311614
## [23,] 0.23238499
## [24,] 0.19806459
## [25,] 0.23085999
## [26,] 0.32775562
## [27,] 0.20173026
## [28,] 0.29246797
## [29,] 0.22558992
## [30,] 0.22455775
## [31,] 0.33479663
## [32,] 0.36228884
## [33,] 0.31738766
## [34,] 0.24855998
## [35,] 0.26848862
## [36,] 0.28541578
## [37,] 0.21215893
## [38,] 0.40517959
## [39,] 0.24904533
## [40,] 0.29082379
## [41,] 0.37626178
## [42,] 0.55183482
## [43,] 0.23486259
## [44,] 0.30686148
## [45,] 0.26628556
## [46,] 0.33098233
## [47,] 0.36388691
## [48,] 0.23912256
## [49,] 0.26184116
## [50,] 0.56558707
## [51,] 0.20680109
## [52,] 0.31344378
## [53,] 0.18309483
## [54,] 0.14490968
## [55,] 0.27790406
## [56,] 0.17501778
## [57,] 0.27759743
## [58,] 0.17935231
## [59,] 0.24590810
## [60,] 0.19717074
## [61,] 0.26663835
## [62,] 0.30755378
## [63,] 0.19095515
## [64,] 0.25070960
## [65,] 0.21361636
## [66,] 0.31447190
## [67,] 0.22181230
## [68,] 0.16500186
## [69,] 0.28215559
## [70,] 0.20664087
## [71,] 0.16246649
## [72,] 0.65383038
## [73,] 0.12966099
## [74,] 0.25077499
## [75,] 0.34635904
## [76,] 0.77230426
## [77,] 0.23309727
## [78,] 0.22270688
## [79,] 0.26791693
## [80,] 0.31113003
## [81,] 0.29849390
## [82,] 0.31540886
## [83,] 0.19842446
## [84,] 0.29431369
## [85,] 0.24903411
## [86,] 0.36310004
## [87,] 0.24156903
## [88,] 0.29265840
## [89,] 0.32228006
## [90,] 0.16797175
## [91,] 0.25339515
## [92,] 0.74747908
## [93,] 0.30800825
## [94,] 0.52256653
## [95,] 0.24207168
## [96,] 0.24224104
## [97,] 0.38309313
## [98,] 0.34272251
## [99,] 0.30855210
## [100,] 0.31332641
## [101,] 0.13175710
## [102,] 0.52616151
## [103,] 0.29251207
## [104,] 0.26759453
## [105,] 0.27034915
## [106,] 0.31709584
## [107,] 0.71710796
## [108,] 0.32771237
## [109,] 0.44799547
## [110,] 0.38583002
## [111,] 0.28309791
## [112,] 0.30278984
## [113,] 0.23373181
## [114,] 0.26564708
## [115,] 0.21650914
## [116,] 0.14249467
## [117,] 0.28816237
## [118,] 0.50146780
## [119,] 0.27275262
## [120,] 0.30931621
## [121,] 0.25642432
## [122,] 0.22868868
## [123,] 0.29927941
## [124,] 0.23602726
## [125,] 0.16619847
## [126,] 0.26127929
## [127,] 0.39811437
## [128,] 0.29907379
## [129,] 0.16099719
## [130,] 0.32120465
## [131,] 0.62121032
## [132,] 0.42720768
## [133,] 0.26921577
## [134,] 0.10672731
## [135,] 0.09403775
## [136,] 0.31611282
## [137,] 0.77719084
## [138,] 0.32150287
## [139,] 0.32584169
## [140,] 0.16565352
## [141,] 0.36621508
## [142,] 0.23320241
## [143,] 0.40794207
## [144,] 0.15695747
## [145,] 0.32928281
## [146,] 0.25502494
## [147,] 0.26832496
## [148,] 0.29441639
## [149,] 0.07488335
## [150,] 0.20697536
## [151,] 0.26660188
## [152,] 0.30600767
## [153,] 0.59134045
## [154,] 0.32003928
## [155,] 0.26273193
## [156,] 0.24144880
## [157,] 0.23044660
## [158,] 0.24723006
## [159,] 0.17413779
## [160,] 0.30400851
## [161,] 0.24419379
## [162,] 0.20031094
## [163,] 0.33161007
## [164,] 0.26401880
## [165,] 0.40979551
## [166,] 0.70736412
## [167,] 0.23345290
## [168,] 1.00000000
## [169,] 0.31928022
## [170,] 0.50754913
## [171,] 0.29095525
## [172,] 0.25984919
## [173,] 0.31825846
## [174,] 0.29242965
## [175,] 0.09371628
## [176,] 0.23990717
## [177,] 0.30406534
## [178,] 0.42256904
## [179,] 0.55104424
## [180,] 0.28741284
## [181,] 0.36200714
## [182,] 0.26220407
## [183,] 0.37525882
## [184,] 0.29735045
## [185,] 0.29436812
## [186,] 0.31080279
## [187,] 0.35616118
## [188,] 0.28424729
## [189,] 0.24819271
## [190,] 0.31402742
## [191,] 0.27160127
## [192,] 0.20536830
## [193,] 0.29436186
## [194,] 0.30705130
## [195,] 0.34614200
## [196,] 0.41139527
## [197,] 0.29562471
## [198,] 0.30759173
## [199,] 0.17055273
## [200,] 0.35225401
## [201,] 0.35911311
## [202,] 0.31442926
## [203,] 0.42306952
## [204,] 0.06659345
## [205,] 0.38868169
## [206,] 0.31464967
## [207,] 0.22548314
## [208,] 0.15799113
## [209,] 0.15728833
## [210,] 0.22359847
## [211,] 0.17150855
## [212,] 0.21236907
## [213,] 0.27109264
## [214,] 0.33711391
## [215,] 0.13048343
## [216,] 0.21336205
## [217,] 0.17684011
## [218,] 0.12887760
## [219,] 0.36176477
## [220,] 0.11578279
## [221,] 0.10425901
## [222,] 0.29537556
## [223,] 0.21472304
## [224,] 0.18308498
## [225,] 0.19961926
## [226,] 0.18963055
## [227,] 0.28114446
## [228,] 0.17855038
## [229,] 0.16717357
## [230,] 0.20194846
## [231,] 0.19534763
## [232,] 0.14242103
## [233,] 0.20446752
## [234,] 0.34641318
## [235,] 0.16923943
## [236,] 0.15481960
## [237,] 0.15171141
## [238,] 0.26525610
## [239,] 0.15534706
## [240,] 0.17756137
## [241,] 0.22552246
## [242,] 0.15296872
## [243,] 0.16226047
## [244,] 0.19536102
## [245,] 0.10873342
## [246,] 0.17854154
## [247,] 0.16896878
## [248,] 0.73678889
## [249,] 0.21334849
## [250,] 0.18991974
## [251,] 0.10188727
## [252,] 0.24783607
## [253,] 0.25153006
## [254,] 0.46005515
## [255,] 0.19881621
## [256,] 0.19861838
## [257,] 0.25887125
## [258,] 0.24146040
## [259,] 0.20561432
## [260,] 0.15708312
## [261,] 0.16982141
## [262,] 0.15302659
## [263,] 0.07961625
## [264,] 0.18787157
## [265,] 0.43192401
## [266,] 0.13838668
## [267,] 0.30165846
## [268,] 0.23526380
## [269,] 0.26675686
## [270,] 0.18036354
## [271,] 0.12287117
## [272,] 0.17950980
## [273,] 0.16462771
## [274,] 0.24715099
## [275,] 0.21116488
## [276,] 0.11171641
## [277,] 0.21437827
## [278,] 0.17049473
## [279,] 0.14242068
## [280,] 0.16650997
## [281,] 0.34271961
## [282,] 0.25748728
## [283,] 0.39569564
## [284,] 0.10625240
## [285,] 0.16380659
## [286,] 0.17799529
## [287,] 0.16845652
## [288,] 0.21498045
## [289,] 0.22465947
## [290,] 0.12999642
## [291,] 0.15041096
## [292,] 0.20147577
## [293,] 0.12880573
## [294,] 0.11596706
## [295,] 0.18587743
## [296,] 0.18400542
## [297,] 0.20677656
## [298,] 0.19267723
## [299,] 0.14579438
## [300,] 0.08685923
## [301,] 0.15201688
## [302,] 0.19473731
## [303,] 0.17907147
## [304,] 0.18134689
## [305,] 0.24069730
## [306,] 0.40939798
## [307,] 0.21703520
## [308,] 0.20363247
## [309,] 0.18075436
## [310,] 0.14525970
## [311,] 0.24178286
## [312,] 0.19545507
## [313,] 0.08559896
## [314,] 0.10438251
## [315,] 0.18477107
## [316,] 0.11548996
## [317,] 0.16054208
## [318,] 0.16756136
## [319,] 0.16079535
## [320,] 0.08983513
## [321,] 0.08079686
## [322,] 0.17676441
## [323,] 0.15844844
## [324,] 0.00000000
## [325,] 0.21800484
## [326,] 0.14413246
## [327,] 0.26179436
## [328,] 0.09588493
## [329,] 0.35976021
## [330,] 0.40227841
## [331,] 0.12568170
## [332,] 0.14071087
## [333,] 0.19034542
## [334,] 0.16669584
## [335,] 0.21339646
## [336,] 0.15276288
## [337,] 0.02206044
## [338,] 0.15241840
## [339,] 0.21909756
## [340,] 0.05712485
## [341,] 0.18989553
## [342,] 0.12251891
## [343,] 0.09695668
## [344,] 0.11888747
## [345,] 0.16129464
## [346,] 0.05716974
## [347,] 0.11884686
## [348,] 0.11261632
## [349,] 0.24261847
## [350,] 0.20260906
## [351,] 0.16462809
## [352,] 0.06283952
## [353,] 0.20778212
## [354,] 0.10309249
## [355,] 0.30639128
## [356,] 0.33232769
## [357,] 0.17120052
## [358,] 0.14161496
## [359,] 0.22299435
## [360,] 0.22711878
## [361,] 0.12538736
## [362,] 0.14474118
## [363,] 0.26098295
## [364,] 0.23411019
## [365,] 0.32043371
## [366,] 0.15149815
## [367,] 0.16367614
## [368,] 0.16032918
## [369,] 0.33282702
## [370,] 0.14579087
## [371,] 0.27831382
## [372,] 0.18315878
## [373,] 0.16397715
## [374,] 0.18828350
## [375,] 0.15657277
## [376,] 0.12504167
## [377,] 0.10450005
## wavelet.HHH_firstorder_RootMeanSquared
## [1,] 0.8118521
## [2,] 0.8199980
## [3,] 0.8378105
## [4,] 0.8173002
## [5,] 0.8282797
## [6,] 0.8641838
## [7,] 0.7755365
## [8,] 0.7977463
## [9,] 0.8369737
## [10,] 0.8183869
## [11,] 0.8821463
## [12,] 0.8426986
## [13,] 0.8167953
## [14,] 0.9051606
## [15,] 0.7708221
## [16,] 0.8503392
## [17,] 0.8539528
## [18,] 0.8323113
## [19,] 0.8200717
## [20,] 0.8322655
## [21,] 0.7854655
## [22,] 0.8289127
## [23,] 0.8141550
## [24,] 0.7414775
## [25,] 0.8089746
## [26,] 0.7952489
## [27,] 0.8019030
## [28,] 0.7858321
## [29,] 0.8002355
## [30,] 0.8183703
## [31,] 0.8403523
## [32,] 0.8694738
## [33,] 0.8359047
## [34,] 0.7931280
## [35,] 0.7821202
## [36,] 0.8163297
## [37,] 0.7905334
## [38,] 0.7608877
## [39,] 0.8169956
## [40,] 0.8134239
## [41,] 0.8307954
## [42,] 0.9294969
## [43,] 0.8873390
## [44,] 0.7656870
## [45,] 0.8190579
## [46,] 0.8262918
## [47,] 0.8363323
## [48,] 0.8167014
## [49,] 0.7742686
## [50,] 0.8527645
## [51,] 0.7473473
## [52,] 0.8191188
## [53,] 0.8597576
## [54,] 0.8070366
## [55,] 0.6751726
## [56,] 0.8315305
## [57,] 0.7996680
## [58,] 0.7739453
## [59,] 0.8243448
## [60,] 0.8197114
## [61,] 0.7771489
## [62,] 0.8298364
## [63,] 0.8279067
## [64,] 0.8241349
## [65,] 0.7855332
## [66,] 0.7877055
## [67,] 0.8219102
## [68,] 0.7809915
## [69,] 0.8006778
## [70,] 0.8157206
## [71,] 0.8013185
## [72,] 0.8194531
## [73,] 0.7497457
## [74,] 0.3001308
## [75,] 0.8095919
## [76,] 0.5835073
## [77,] 0.7987754
## [78,] 0.7711233
## [79,] 0.8044806
## [80,] 0.8169393
## [81,] 0.8493163
## [82,] 0.8185873
## [83,] 0.2718735
## [84,] 0.8329841
## [85,] 0.8015162
## [86,] 0.8046705
## [87,] 0.8320715
## [88,] 0.7923592
## [89,] 0.8278208
## [90,] 0.6389778
## [91,] 0.7923957
## [92,] 0.2988207
## [93,] 0.7975279
## [94,] 0.6519222
## [95,] 0.8107011
## [96,] 0.8242896
## [97,] 0.6378107
## [98,] 0.8284916
## [99,] 0.8382996
## [100,] 0.8106929
## [101,] 0.7586937
## [102,] 0.6636864
## [103,] 0.8163756
## [104,] 0.7817834
## [105,] 0.8321141
## [106,] 0.8562617
## [107,] 0.9171376
## [108,] 0.7630692
## [109,] 0.7900912
## [110,] 0.6912840
## [111,] 0.8034859
## [112,] 0.8259310
## [113,] 0.7970551
## [114,] 0.7967517
## [115,] 0.7067613
## [116,] 0.8927441
## [117,] 0.8200365
## [118,] 0.7674661
## [119,] 0.7956960
## [120,] 0.8046352
## [121,] 0.8200748
## [122,] 0.7999352
## [123,] 0.8221281
## [124,] 0.8280554
## [125,] 0.6258464
## [126,] 0.8080255
## [127,] 0.8085887
## [128,] 0.8010081
## [129,] 0.7368625
## [130,] 0.7315539
## [131,] 0.5737401
## [132,] 0.5880461
## [133,] 0.8401371
## [134,] 0.8783158
## [135,] 0.6610089
## [136,] 0.7572359
## [137,] 0.8575147
## [138,] 0.6842666
## [139,] 0.8555388
## [140,] 0.7997245
## [141,] 0.7838992
## [142,] 0.7517563
## [143,] 0.4616655
## [144,] 0.8033638
## [145,] 0.9097368
## [146,] 0.8316764
## [147,] 0.8358507
## [148,] 0.8360723
## [149,] 0.8281186
## [150,] 0.7893164
## [151,] 0.7667148
## [152,] 0.8615634
## [153,] 0.7456305
## [154,] 0.8279977
## [155,] 0.7645100
## [156,] 0.8293194
## [157,] 0.7622282
## [158,] 0.9808291
## [159,] 0.8128420
## [160,] 0.8446673
## [161,] 0.7997340
## [162,] 0.8743980
## [163,] 0.8620595
## [164,] 0.8090438
## [165,] 0.8459719
## [166,] 0.9866376
## [167,] 0.8471941
## [168,] 0.7084401
## [169,] 0.8567124
## [170,] 0.9021429
## [171,] 0.8026076
## [172,] 0.7954652
## [173,] 0.8068738
## [174,] 0.8291687
## [175,] 0.7911142
## [176,] 0.8376993
## [177,] 0.7716944
## [178,] 0.8153163
## [179,] 1.0000000
## [180,] 0.7948871
## [181,] 0.8680201
## [182,] 0.8218950
## [183,] 0.8431052
## [184,] 0.8499392
## [185,] 0.7095399
## [186,] 0.7685803
## [187,] 0.8383074
## [188,] 0.8324388
## [189,] 0.7827440
## [190,] 0.8528224
## [191,] 0.8384152
## [192,] 0.8101219
## [193,] 0.8112677
## [194,] 0.8574972
## [195,] 0.7871006
## [196,] 0.7771342
## [197,] 0.8351670
## [198,] 0.8635046
## [199,] 0.7828515
## [200,] 0.8258920
## [201,] 0.8255532
## [202,] 0.8623547
## [203,] 0.7328438
## [204,] 0.8915825
## [205,] 0.8124635
## [206,] 0.8196761
## [207,] 0.8315875
## [208,] 0.7708181
## [209,] 0.8750952
## [210,] 0.8039997
## [211,] 0.7837567
## [212,] 0.8150308
## [213,] 0.8762191
## [214,] 0.8386183
## [215,] 0.7600856
## [216,] 0.8191023
## [217,] 0.7935140
## [218,] 0.7683698
## [219,] 0.8412102
## [220,] 0.9061573
## [221,] 0.8233189
## [222,] 0.7621326
## [223,] 0.8190146
## [224,] 0.7194731
## [225,] 0.7007337
## [226,] 0.7971404
## [227,] 0.7988208
## [228,] 0.7727031
## [229,] 0.8526346
## [230,] 0.8185006
## [231,] 0.8015007
## [232,] 0.7773175
## [233,] 0.8799228
## [234,] 0.8727718
## [235,] 0.7512759
## [236,] 0.7484088
## [237,] 0.7097269
## [238,] 0.9845238
## [239,] 0.7913517
## [240,] 0.6785962
## [241,] 0.7841871
## [242,] 0.8435541
## [243,] 0.8131938
## [244,] 0.8001981
## [245,] 0.8506558
## [246,] 0.8038453
## [247,] 0.8050028
## [248,] 0.1961286
## [249,] 0.0000000
## [250,] 0.8234405
## [251,] 0.7577756
## [252,] 0.7947347
## [253,] 0.8503053
## [254,] 0.1161712
## [255,] 0.7838433
## [256,] 0.8017049
## [257,] 0.8193397
## [258,] 0.8079151
## [259,] 0.7469377
## [260,] 0.8077100
## [261,] 0.8102789
## [262,] 0.7996352
## [263,] 0.8404753
## [264,] 0.7764524
## [265,] 0.8011387
## [266,] 0.7573704
## [267,] 0.8107955
## [268,] 0.8225913
## [269,] 0.8149509
## [270,] 0.8049109
## [271,] 0.3449559
## [272,] 0.7826617
## [273,] 0.8139712
## [274,] 0.7494178
## [275,] 0.8010431
## [276,] 0.7660445
## [277,] 0.8083646
## [278,] 0.7518370
## [279,] 0.7725772
## [280,] 0.7072119
## [281,] 0.7518827
## [282,] 0.9092533
## [283,] 0.8004763
## [284,] 0.7135125
## [285,] 0.7702921
## [286,] 0.7222514
## [287,] 0.5721304
## [288,] 0.6778741
## [289,] 0.7891610
## [290,] 0.7212973
## [291,] 0.7485299
## [292,] 0.7968483
## [293,] 0.8671728
## [294,] 0.8131405
## [295,] 0.8121827
## [296,] 0.7284122
## [297,] 0.7483256
## [298,] 0.8284500
## [299,] 0.8142392
## [300,] 0.8468520
## [301,] 0.8050368
## [302,] 0.7858307
## [303,] 0.7515265
## [304,] 0.8144326
## [305,] 0.7257758
## [306,] 0.5982949
## [307,] 0.7829234
## [308,] 0.8018308
## [309,] 0.6517652
## [310,] 0.8104412
## [311,] 0.7949570
## [312,] 0.7909373
## [313,] 0.6665431
## [314,] 0.7145172
## [315,] 0.7523379
## [316,] 0.7756434
## [317,] 0.6602434
## [318,] 0.7914396
## [319,] 0.7638126
## [320,] 0.7654897
## [321,] 0.6786492
## [322,] 0.8120604
## [323,] 0.7526800
## [324,] 0.6354545
## [325,] 0.7966333
## [326,] 0.8040031
## [327,] 0.5543962
## [328,] 0.7319796
## [329,] 0.7619096
## [330,] 0.5829308
## [331,] 0.7913121
## [332,] 0.7631589
## [333,] 0.7898192
## [334,] 0.7819943
## [335,] 0.7982551
## [336,] 0.8185685
## [337,] 0.8335828
## [338,] 0.7924966
## [339,] 0.7999721
## [340,] 0.7718021
## [341,] 0.7352843
## [342,] 0.7750705
## [343,] 0.7220507
## [344,] 0.8479851
## [345,] 0.7874475
## [346,] 0.7645828
## [347,] 0.7776843
## [348,] 0.7712223
## [349,] 0.8121598
## [350,] 0.8151909
## [351,] 0.8054588
## [352,] 0.6316290
## [353,] 0.8158128
## [354,] 0.7798804
## [355,] 0.6110440
## [356,] 0.6599393
## [357,] 0.7901682
## [358,] 0.7724755
## [359,] 0.8064197
## [360,] 0.8451124
## [361,] 0.7603884
## [362,] 0.8363994
## [363,] 0.8158606
## [364,] 0.8229340
## [365,] 0.7658555
## [366,] 0.8136259
## [367,] 0.7211158
## [368,] 0.7623843
## [369,] 0.8170678
## [370,] 0.7021389
## [371,] 0.4514798
## [372,] 0.7232640
## [373,] 0.7825575
## [374,] 0.7780419
## [375,] 0.7809053
## [376,] 0.7600575
## [377,] 0.7605674
## wavelet.LHL_gldm_DependenceVariance
## [1,] 0.366189418
## [2,] 0.412522527
## [3,] 0.196159739
## [4,] 0.281513440
## [5,] 0.327016595
## [6,] 0.256379110
## [7,] 0.593272734
## [8,] 0.313685466
## [9,] 0.240015530
## [10,] 0.356621173
## [11,] 0.163976351
## [12,] 0.351430142
## [13,] 0.387980999
## [14,] 0.059387027
## [15,] 0.370940800
## [16,] 0.206658051
## [17,] 0.273020393
## [18,] 0.459789730
## [19,] 0.501706615
## [20,] 0.370106547
## [21,] 0.216606806
## [22,] 0.506377536
## [23,] 0.531148291
## [24,] 0.457462095
## [25,] 0.609970116
## [26,] 0.540680114
## [27,] 0.889496530
## [28,] 0.717621726
## [29,] 0.720732183
## [30,] 0.511795896
## [31,] 0.678828398
## [32,] 0.252516998
## [33,] 0.619545392
## [34,] 0.457729600
## [35,] 0.544352564
## [36,] 0.553512152
## [37,] 0.633530051
## [38,] 0.583337685
## [39,] 0.616029958
## [40,] 0.325863949
## [41,] 0.460675576
## [42,] 0.048500298
## [43,] 0.218767950
## [44,] 0.558327080
## [45,] 0.320766508
## [46,] 0.366002802
## [47,] 0.699577909
## [48,] 0.482944998
## [49,] 0.355029899
## [50,] 0.340724882
## [51,] 0.774800059
## [52,] 0.370215196
## [53,] 0.098082962
## [54,] 0.414861214
## [55,] 0.530610801
## [56,] 0.779914256
## [57,] 0.542043128
## [58,] 0.473740459
## [59,] 0.278420639
## [60,] 0.488889001
## [61,] 0.588722907
## [62,] 0.127304160
## [63,] 0.566104591
## [64,] 0.471952876
## [65,] 0.669645185
## [66,] 0.237200149
## [67,] 0.480204337
## [68,] 0.267752329
## [69,] 0.351962047
## [70,] 0.420898997
## [71,] 0.241331751
## [72,] 0.505633025
## [73,] 0.639588290
## [74,] 0.043936642
## [75,] 0.463518178
## [76,] 0.008996976
## [77,] 0.578016003
## [78,] 0.623284778
## [79,] 0.688430470
## [80,] 0.581450049
## [81,] 0.695914842
## [82,] 0.304501147
## [83,] 0.086378181
## [84,] 0.458983246
## [85,] 0.512848458
## [86,] 0.127381009
## [87,] 0.264359193
## [88,] 0.541038921
## [89,] 0.237184466
## [90,] 0.167823882
## [91,] 0.744589069
## [92,] 0.464604005
## [93,] 0.230331543
## [94,] 0.122218335
## [95,] 0.654438223
## [96,] 0.285502227
## [97,] 0.156012119
## [98,] 0.229379967
## [99,] 0.204575557
## [100,] 0.620608936
## [101,] 0.282513539
## [102,] 0.043666249
## [103,] 0.308337837
## [104,] 0.446009078
## [105,] 0.215513111
## [106,] 0.543351117
## [107,] 0.488488977
## [108,] 0.206045588
## [109,] 0.058515261
## [110,] 0.116936376
## [111,] 0.366402835
## [112,] 0.294682594
## [113,] 0.188050028
## [114,] 0.273795619
## [115,] 0.238525440
## [116,] 0.739772252
## [117,] 0.455389140
## [118,] 0.121848496
## [119,] 0.291947065
## [120,] 0.326064968
## [121,] 0.122341327
## [122,] 0.383454393
## [123,] 0.293373094
## [124,] 0.143698707
## [125,] 0.397916555
## [126,] 0.330058202
## [127,] 0.246193235
## [128,] 0.521802918
## [129,] 0.314947761
## [130,] 0.725986268
## [131,] 0.132497136
## [132,] 0.675371766
## [133,] 0.561945758
## [134,] 0.064651042
## [135,] 0.098048803
## [136,] 0.529863985
## [137,] 0.172492021
## [138,] 0.243124799
## [139,] 0.301185017
## [140,] 0.215804386
## [141,] 0.248248847
## [142,] 0.376920680
## [143,] 0.091855205
## [144,] 0.595013924
## [145,] 0.050873262
## [146,] 0.119046253
## [147,] 0.181196864
## [148,] 0.624814846
## [149,] 0.234356882
## [150,] 0.424258089
## [151,] 0.081026687
## [152,] 0.478401284
## [153,] 0.026515605
## [154,] 0.510780210
## [155,] 0.032052108
## [156,] 0.133467384
## [157,] 0.350821659
## [158,] 0.237611570
## [159,] 0.572505488
## [160,] 0.131319015
## [161,] 0.363715397
## [162,] 0.055421433
## [163,] 0.093793670
## [164,] 0.186981767
## [165,] 0.185647813
## [166,] 0.008461042
## [167,] 0.176485667
## [168,] 0.033928214
## [169,] 0.077791885
## [170,] 0.146591145
## [171,] 0.115708226
## [172,] 0.287906209
## [173,] 0.555504996
## [174,] 0.140728622
## [175,] 0.217754818
## [176,] 0.352852810
## [177,] 0.116542358
## [178,] 0.117980536
## [179,] 0.000000000
## [180,] 0.507866719
## [181,] 0.199437460
## [182,] 0.281487658
## [183,] 0.243862694
## [184,] 0.099218192
## [185,] 0.237538911
## [186,] 0.065100916
## [187,] 0.157801259
## [188,] 0.041615929
## [189,] 0.054464181
## [190,] 0.199947299
## [191,] 0.120376747
## [192,] 0.438412122
## [193,] 0.176500890
## [194,] 0.087874839
## [195,] 0.400167075
## [196,] 0.295790719
## [197,] 0.197758122
## [198,] 0.089355490
## [199,] 0.273163370
## [200,] 0.223957846
## [201,] 0.080573688
## [202,] 0.095163847
## [203,] 0.157378711
## [204,] 0.144791406
## [205,] 0.324314617
## [206,] 0.078950403
## [207,] 0.135511938
## [208,] 0.210823140
## [209,] 0.226979014
## [210,] 0.256264680
## [211,] 0.214119352
## [212,] 0.331950720
## [213,] 0.169560678
## [214,] 0.443453384
## [215,] 0.349883815
## [216,] 0.198144279
## [217,] 0.398974366
## [218,] 0.278830568
## [219,] 0.337242031
## [220,] 0.182867957
## [221,] 0.277846453
## [222,] 0.130525024
## [223,] 0.087195913
## [224,] 0.273617321
## [225,] 0.620010736
## [226,] 0.299363539
## [227,] 0.386274910
## [228,] 0.324453947
## [229,] 0.264338145
## [230,] 0.502668857
## [231,] 0.223320466
## [232,] 0.282787767
## [233,] 0.300131620
## [234,] 0.201981449
## [235,] 0.137019203
## [236,] 0.523632391
## [237,] 0.266150254
## [238,] 0.173247076
## [239,] 0.357992732
## [240,] 0.268444770
## [241,] 0.796760372
## [242,] 0.448369324
## [243,] 0.240572342
## [244,] 0.263858500
## [245,] 0.271054522
## [246,] 0.575220569
## [247,] 0.119690262
## [248,] 0.101100171
## [249,] 0.107303588
## [250,] 0.561218931
## [251,] 0.251671282
## [252,] 0.175424543
## [253,] 0.279090727
## [254,] 0.058354602
## [255,] 0.278538880
## [256,] 0.480274837
## [257,] 0.438415563
## [258,] 0.672911996
## [259,] 0.427988160
## [260,] 0.431867588
## [261,] 0.164947615
## [262,] 0.413210343
## [263,] 0.301562151
## [264,] 0.263734985
## [265,] 0.280589700
## [266,] 0.263495951
## [267,] 0.384115077
## [268,] 0.415500471
## [269,] 0.238309311
## [270,] 0.117153409
## [271,] 0.094037054
## [272,] 0.402141890
## [273,] 0.266011752
## [274,] 0.033322573
## [275,] 0.180520809
## [276,] 0.294381615
## [277,] 0.250642370
## [278,] 0.681116360
## [279,] 0.330244384
## [280,] 0.380987330
## [281,] 0.585738294
## [282,] 0.496468759
## [283,] 0.419424815
## [284,] 0.660015358
## [285,] 0.390467710
## [286,] 0.356668469
## [287,] 0.108552143
## [288,] 0.061759655
## [289,] 0.730999438
## [290,] 0.733588598
## [291,] 0.351767628
## [292,] 0.293504687
## [293,] 0.063558396
## [294,] 0.159941581
## [295,] 0.246413845
## [296,] 0.584186361
## [297,] 0.305016190
## [298,] 0.157901652
## [299,] 0.256776692
## [300,] 0.246507834
## [301,] 0.094287506
## [302,] 0.291589496
## [303,] 0.369624398
## [304,] 0.296167788
## [305,] 0.057642566
## [306,] 0.027846385
## [307,] 0.440866310
## [308,] 0.203242861
## [309,] 0.220024688
## [310,] 0.407047766
## [311,] 0.539089622
## [312,] 0.242959340
## [313,] 0.579885527
## [314,] 0.945188078
## [315,] 0.412584638
## [316,] 0.696501504
## [317,] 0.309721127
## [318,] 0.516607571
## [319,] 0.759762917
## [320,] 0.420128754
## [321,] 0.444971028
## [322,] 0.321909675
## [323,] 1.000000000
## [324,] 0.609498580
## [325,] 0.243314890
## [326,] 0.592177282
## [327,] 0.168081599
## [328,] 0.513453282
## [329,] 0.252839872
## [330,] 0.017497686
## [331,] 0.414827390
## [332,] 0.517260246
## [333,] 0.718599617
## [334,] 0.683861078
## [335,] 0.685130952
## [336,] 0.777332392
## [337,] 0.489774978
## [338,] 0.628708260
## [339,] 0.495925083
## [340,] 0.910341834
## [341,] 0.160037244
## [342,] 0.661244231
## [343,] 0.168971251
## [344,] 0.174058054
## [345,] 0.498787426
## [346,] 0.189030554
## [347,] 0.437915795
## [348,] 0.931538050
## [349,] 0.337396799
## [350,] 0.101937141
## [351,] 0.593669528
## [352,] 0.486665008
## [353,] 0.622073882
## [354,] 0.519073884
## [355,] 0.241724071
## [356,] 0.065369314
## [357,] 0.744320816
## [358,] 0.842999056
## [359,] 0.667071031
## [360,] 0.260631147
## [361,] 0.472453760
## [362,] 0.546070021
## [363,] 0.242432685
## [364,] 0.239973698
## [365,] 0.445872735
## [366,] 0.389675020
## [367,] 0.435311626
## [368,] 0.876673473
## [369,] 0.178325262
## [370,] 0.549656477
## [371,] 0.084363663
## [372,] 0.882025055
## [373,] 0.695992440
## [374,] 0.562188238
## [375,] 0.724712388
## [376,] 0.610848417
## [377,] 0.641753063
##
## Slot "notNA":
## age original_shape_VoxelVolume wavelet.HHL_glcm_ClusterProminence
## LUNG1-001 1 1 1
## LUNG1-002 1 1 1
## LUNG1-003 1 1 1
## LUNG1-004 1 1 1
## LUNG1-005 1 1 1
## LUNG1-006 1 1 1
## LUNG1-007 1 1 1
## LUNG1-008 1 1 1
## LUNG1-009 1 1 1
## LUNG1-010 1 1 1
## LUNG1-011 1 1 1
## LUNG1-012 1 1 1
## LUNG1-013 1 1 1
## LUNG1-014 1 1 1
## LUNG1-015 1 1 1
## LUNG1-016 1 1 1
## LUNG1-017 1 1 1
## LUNG1-018 1 1 1
## LUNG1-019 1 1 1
## LUNG1-022 1 1 1
## LUNG1-024 1 1 1
## LUNG1-025 1 1 1
## LUNG1-026 1 1 1
## LUNG1-029 1 1 1
## LUNG1-030 1 1 1
## LUNG1-031 1 1 1
## LUNG1-032 1 1 1
## LUNG1-033 1 1 1
## LUNG1-034 1 1 1
## LUNG1-035 1 1 1
## LUNG1-036 1 1 1
## LUNG1-037 1 1 1
## LUNG1-038 1 1 1
## LUNG1-040 1 1 1
## LUNG1-041 1 1 1
## LUNG1-042 1 1 1
## LUNG1-043 1 1 1
## LUNG1-045 1 1 1
## LUNG1-046 1 1 1
## LUNG1-047 1 1 1
## LUNG1-048 1 1 1
## LUNG1-049 1 1 1
## LUNG1-050 1 1 1
## LUNG1-051 1 1 1
## LUNG1-052 1 1 1
## LUNG1-053 1 1 1
## LUNG1-054 1 1 1
## LUNG1-055 1 1 1
## LUNG1-057 1 1 1
## LUNG1-058 1 1 1
## LUNG1-059 1 1 1
## LUNG1-060 1 1 1
## LUNG1-061 1 1 1
## LUNG1-062 1 1 1
## LUNG1-063 1 1 1
## LUNG1-064 1 1 1
## LUNG1-065 1 1 1
## LUNG1-067 1 1 1
## LUNG1-068 1 1 1
## LUNG1-070 1 1 1
## LUNG1-071 1 1 1
## LUNG1-072 1 1 1
## LUNG1-073 1 1 1
## LUNG1-074 1 1 1
## LUNG1-075 1 1 1
## LUNG1-076 1 1 1
## LUNG1-077 1 1 1
## LUNG1-078 1 1 1
## LUNG1-079 1 1 1
## LUNG1-081 1 1 1
## LUNG1-082 1 1 1
## LUNG1-084 1 1 1
## LUNG1-085 1 1 1
## LUNG1-086 1 1 1
## LUNG1-088 1 1 1
## LUNG1-089 1 1 1
## LUNG1-091 1 1 1
## LUNG1-092 1 1 1
## LUNG1-093 1 1 1
## LUNG1-095 1 1 1
## LUNG1-096 1 1 1
## LUNG1-097 1 1 1
## LUNG1-098 1 1 1
## LUNG1-099 1 1 1
## LUNG1-101 1 1 1
## LUNG1-102 1 1 1
## LUNG1-103 1 1 1
## LUNG1-104 1 1 1
## LUNG1-105 1 1 1
## LUNG1-106 1 1 1
## LUNG1-107 1 1 1
## LUNG1-108 1 1 1
## LUNG1-109 1 1 1
## LUNG1-110 1 1 1
## LUNG1-112 1 1 1
## LUNG1-113 1 1 1
## LUNG1-114 1 1 1
## LUNG1-115 1 1 1
## LUNG1-116 1 1 1
## LUNG1-117 1 1 1
## LUNG1-118 1 1 1
## LUNG1-119 1 1 1
## LUNG1-120 1 1 1
## LUNG1-121 1 1 1
## LUNG1-122 1 1 1
## LUNG1-123 1 1 1
## LUNG1-124 1 1 1
## LUNG1-125 1 1 1
## LUNG1-126 1 1 1
## LUNG1-127 1 1 1
## LUNG1-129 1 1 1
## LUNG1-130 1 1 1
## LUNG1-131 1 1 1
## LUNG1-132 1 1 1
## LUNG1-134 1 1 1
## LUNG1-135 1 1 1
## LUNG1-137 1 1 1
## LUNG1-139 1 1 1
## LUNG1-140 1 1 1
## LUNG1-141 1 1 1
## LUNG1-142 1 1 1
## LUNG1-143 1 1 1
## LUNG1-144 1 1 1
## LUNG1-145 1 1 1
## LUNG1-146 1 1 1
## LUNG1-147 1 1 1
## LUNG1-148 1 1 1
## LUNG1-149 1 1 1
## LUNG1-150 1 1 1
## LUNG1-151 1 1 1
## LUNG1-152 1 1 1
## LUNG1-153 1 1 1
## LUNG1-155 1 1 1
## LUNG1-156 1 1 1
## LUNG1-158 1 1 1
## LUNG1-159 1 1 1
## LUNG1-160 1 1 1
## LUNG1-162 1 1 1
## LUNG1-163 1 1 1
## LUNG1-164 1 1 1
## LUNG1-165 1 1 1
## LUNG1-166 1 1 1
## LUNG1-167 1 1 1
## LUNG1-168 1 1 1
## LUNG1-169 1 1 1
## LUNG1-170 1 1 1
## LUNG1-172 1 1 1
## LUNG1-173 1 1 1
## LUNG1-174 1 1 1
## LUNG1-175 1 1 1
## LUNG1-176 1 1 1
## LUNG1-177 1 1 1
## LUNG1-178 1 1 1
## LUNG1-179 1 1 1
## LUNG1-180 1 1 1
## LUNG1-181 1 1 1
## LUNG1-182 1 1 1
## LUNG1-183 1 1 1
## LUNG1-184 1 1 1
## LUNG1-186 1 1 1
## LUNG1-187 1 1 1
## LUNG1-188 1 1 1
## LUNG1-189 1 1 1
## LUNG1-190 1 1 1
## LUNG1-191 1 1 1
## LUNG1-192 1 1 1
## LUNG1-193 1 1 1
## LUNG1-195 1 1 1
## LUNG1-196 1 1 1
## LUNG1-197 1 1 1
## LUNG1-198 1 1 1
## LUNG1-199 1 1 1
## LUNG1-200 1 1 1
## LUNG1-201 1 1 1
## LUNG1-202 1 1 1
## LUNG1-203 1 1 1
## LUNG1-204 1 1 1
## LUNG1-205 1 1 1
## LUNG1-206 1 1 1
## LUNG1-207 1 1 1
## LUNG1-208 1 1 1
## LUNG1-209 1 1 1
## LUNG1-210 1 1 1
## LUNG1-211 1 1 1
## LUNG1-212 1 1 1
## LUNG1-213 1 1 1
## LUNG1-214 1 1 1
## LUNG1-215 1 1 1
## LUNG1-216 1 1 1
## LUNG1-217 1 1 1
## LUNG1-218 1 1 1
## LUNG1-219 1 1 1
## LUNG1-220 1 1 1
## LUNG1-221 1 1 1
## LUNG1-222 1 1 1
## LUNG1-223 1 1 1
## LUNG1-224 1 1 1
## LUNG1-225 1 1 1
## LUNG1-226 1 1 1
## LUNG1-227 1 1 1
## LUNG1-228 1 1 1
## LUNG1-229 1 1 1
## LUNG1-230 1 1 1
## LUNG1-231 1 1 1
## LUNG1-232 1 1 1
## LUNG1-233 1 1 1
## LUNG1-234 1 1 1
## LUNG1-235 1 1 1
## LUNG1-236 1 1 1
## LUNG1-237 1 1 1
## LUNG1-238 1 1 1
## LUNG1-239 1 1 1
## LUNG1-240 1 1 1
## LUNG1-242 1 1 1
## LUNG1-243 1 1 1
## LUNG1-244 1 1 1
## LUNG1-245 1 1 1
## LUNG1-247 1 1 1
## LUNG1-248 1 1 1
## LUNG1-250 1 1 1
## LUNG1-251 1 1 1
## LUNG1-252 1 1 1
## LUNG1-253 1 1 1
## LUNG1-254 1 1 1
## LUNG1-256 1 1 1
## LUNG1-257 1 1 1
## LUNG1-258 1 1 1
## LUNG1-259 1 1 1
## LUNG1-261 1 1 1
## LUNG1-262 1 1 1
## LUNG1-263 1 1 1
## LUNG1-264 1 1 1
## LUNG1-265 1 1 1
## LUNG1-266 1 1 1
## LUNG1-267 1 1 1
## LUNG1-268 1 1 1
## LUNG1-269 1 1 1
## LUNG1-270 1 1 1
## LUNG1-271 1 1 1
## LUNG1-272 1 1 1
## LUNG1-273 1 1 1
## LUNG1-274 1 1 1
## LUNG1-275 1 1 1
## LUNG1-276 1 1 1
## LUNG1-277 1 1 1
## LUNG1-279 1 1 1
## LUNG1-280 1 1 1
## LUNG1-281 1 1 1
## LUNG1-282 1 1 1
## LUNG1-283 1 1 1
## LUNG1-284 1 1 1
## LUNG1-285 1 1 1
## LUNG1-286 1 1 1
## LUNG1-288 1 1 1
## LUNG1-289 1 1 1
## LUNG1-290 1 1 1
## LUNG1-291 1 1 1
## LUNG1-292 1 1 1
## LUNG1-293 1 1 1
## LUNG1-294 1 1 1
## LUNG1-295 1 1 1
## LUNG1-296 1 1 1
## LUNG1-297 1 1 1
## LUNG1-298 1 1 1
## LUNG1-299 1 1 1
## LUNG1-300 1 1 1
## LUNG1-301 1 1 1
## LUNG1-302 1 1 1
## LUNG1-303 1 1 1
## LUNG1-304 1 1 1
## LUNG1-307 1 1 1
## LUNG1-308 1 1 1
## LUNG1-310 1 1 1
## LUNG1-311 1 1 1
## LUNG1-312 1 1 1
## LUNG1-313 1 1 1
## LUNG1-314 1 1 1
## LUNG1-316 1 1 1
## LUNG1-317 1 1 1
## LUNG1-318 1 1 1
## LUNG1-319 1 1 1
## LUNG1-320 1 1 1
## LUNG1-321 1 1 1
## LUNG1-322 1 1 1
## LUNG1-323 1 1 1
## LUNG1-324 1 1 1
## LUNG1-325 1 1 1
## LUNG1-326 1 1 1
## LUNG1-327 1 1 1
## LUNG1-329 1 1 1
## LUNG1-330 1 1 1
## LUNG1-331 1 1 1
## LUNG1-333 1 1 1
## LUNG1-334 1 1 1
## LUNG1-335 1 1 1
## LUNG1-336 1 1 1
## LUNG1-337 1 1 1
## LUNG1-339 1 1 1
## LUNG1-340 1 1 1
## LUNG1-341 1 1 1
## LUNG1-342 1 1 1
## LUNG1-343 1 1 1
## LUNG1-344 1 1 1
## LUNG1-345 1 1 1
## LUNG1-346 1 1 1
## LUNG1-347 1 1 1
## LUNG1-348 1 1 1
## LUNG1-349 1 1 1
## LUNG1-350 1 1 1
## LUNG1-351 1 1 1
## LUNG1-352 1 1 1
## LUNG1-354 1 1 1
## LUNG1-355 1 1 1
## LUNG1-356 1 1 1
## LUNG1-357 1 1 1
## LUNG1-358 1 1 1
## LUNG1-359 1 1 1
## LUNG1-360 1 1 1
## LUNG1-361 1 1 1
## LUNG1-362 1 1 1
## LUNG1-363 1 1 1
## LUNG1-364 1 1 1
## LUNG1-365 1 1 1
## LUNG1-366 1 1 1
## LUNG1-367 1 1 1
## LUNG1-368 1 1 1
## LUNG1-369 1 1 1
## LUNG1-370 1 1 1
## LUNG1-371 1 1 1
## LUNG1-372 1 1 1
## LUNG1-373 1 1 1
## LUNG1-374 1 1 1
## LUNG1-375 1 1 1
## LUNG1-376 1 1 1
## LUNG1-377 1 1 1
## LUNG1-378 1 1 1
## LUNG1-379 1 1 1
## LUNG1-380 1 1 1
## LUNG1-381 1 1 1
## LUNG1-382 1 1 1
## LUNG1-383 1 1 1
## LUNG1-384 1 1 1
## LUNG1-385 1 1 1
## LUNG1-386 1 1 1
## LUNG1-387 1 1 1
## LUNG1-388 1 1 1
## LUNG1-389 1 1 1
## LUNG1-390 1 1 1
## LUNG1-391 1 1 1
## LUNG1-392 1 1 1
## LUNG1-393 1 1 1
## LUNG1-395 1 1 1
## LUNG1-396 1 1 1
## LUNG1-397 1 1 1
## LUNG1-398 1 1 1
## LUNG1-399 1 1 1
## LUNG1-400 1 1 1
## LUNG1-401 1 1 1
## LUNG1-403 1 1 1
## LUNG1-404 1 1 1
## LUNG1-405 1 1 1
## LUNG1-406 1 1 1
## LUNG1-407 1 1 1
## LUNG1-408 1 1 1
## LUNG1-409 1 1 1
## LUNG1-410 1 1 1
## LUNG1-411 1 1 1
## LUNG1-412 1 1 1
## LUNG1-413 1 1 1
## LUNG1-414 1 1 1
## LUNG1-415 1 1 1
## LUNG1-416 1 1 1
## LUNG1-417 1 1 1
## LUNG1-418 1 1 1
## LUNG1-419 1 1 1
## LUNG1-420 1 1 1
## LUNG1-421 1 1 1
## log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis
## LUNG1-001 1
## LUNG1-002 1
## LUNG1-003 1
## LUNG1-004 1
## LUNG1-005 1
## LUNG1-006 1
## LUNG1-007 1
## LUNG1-008 1
## LUNG1-009 1
## LUNG1-010 1
## LUNG1-011 1
## LUNG1-012 1
## LUNG1-013 1
## LUNG1-014 1
## LUNG1-015 1
## LUNG1-016 1
## LUNG1-017 1
## LUNG1-018 1
## LUNG1-019 1
## LUNG1-022 1
## LUNG1-024 1
## LUNG1-025 1
## LUNG1-026 1
## LUNG1-029 1
## LUNG1-030 1
## LUNG1-031 1
## LUNG1-032 1
## LUNG1-033 1
## LUNG1-034 1
## LUNG1-035 1
## LUNG1-036 1
## LUNG1-037 1
## LUNG1-038 1
## LUNG1-040 1
## LUNG1-041 1
## LUNG1-042 1
## LUNG1-043 1
## LUNG1-045 1
## LUNG1-046 1
## LUNG1-047 1
## LUNG1-048 1
## LUNG1-049 1
## LUNG1-050 1
## LUNG1-051 1
## LUNG1-052 1
## LUNG1-053 1
## LUNG1-054 1
## LUNG1-055 1
## LUNG1-057 1
## LUNG1-058 1
## LUNG1-059 1
## LUNG1-060 1
## LUNG1-061 1
## LUNG1-062 1
## LUNG1-063 1
## LUNG1-064 1
## LUNG1-065 1
## LUNG1-067 1
## LUNG1-068 1
## LUNG1-070 1
## LUNG1-071 1
## LUNG1-072 1
## LUNG1-073 1
## LUNG1-074 1
## LUNG1-075 1
## LUNG1-076 1
## LUNG1-077 1
## LUNG1-078 1
## LUNG1-079 1
## LUNG1-081 1
## LUNG1-082 1
## LUNG1-084 1
## LUNG1-085 1
## LUNG1-086 1
## LUNG1-088 1
## LUNG1-089 1
## LUNG1-091 1
## LUNG1-092 1
## LUNG1-093 1
## LUNG1-095 1
## LUNG1-096 1
## LUNG1-097 1
## LUNG1-098 1
## LUNG1-099 1
## LUNG1-101 1
## LUNG1-102 1
## LUNG1-103 1
## LUNG1-104 1
## LUNG1-105 1
## LUNG1-106 1
## LUNG1-107 1
## LUNG1-108 1
## LUNG1-109 1
## LUNG1-110 1
## LUNG1-112 1
## LUNG1-113 1
## LUNG1-114 1
## LUNG1-115 1
## LUNG1-116 1
## LUNG1-117 1
## LUNG1-118 1
## LUNG1-119 1
## LUNG1-120 1
## LUNG1-121 1
## LUNG1-122 1
## LUNG1-123 1
## LUNG1-124 1
## LUNG1-125 1
## LUNG1-126 1
## LUNG1-127 1
## LUNG1-129 1
## LUNG1-130 1
## LUNG1-131 1
## LUNG1-132 1
## LUNG1-134 1
## LUNG1-135 1
## LUNG1-137 1
## LUNG1-139 1
## LUNG1-140 1
## LUNG1-141 1
## LUNG1-142 1
## LUNG1-143 1
## LUNG1-144 1
## LUNG1-145 1
## LUNG1-146 1
## LUNG1-147 1
## LUNG1-148 1
## LUNG1-149 1
## LUNG1-150 1
## LUNG1-151 1
## LUNG1-152 1
## LUNG1-153 1
## LUNG1-155 1
## LUNG1-156 1
## LUNG1-158 1
## LUNG1-159 1
## LUNG1-160 1
## LUNG1-162 1
## LUNG1-163 1
## LUNG1-164 1
## LUNG1-165 1
## LUNG1-166 1
## LUNG1-167 1
## LUNG1-168 1
## LUNG1-169 1
## LUNG1-170 1
## LUNG1-172 1
## LUNG1-173 1
## LUNG1-174 1
## LUNG1-175 1
## LUNG1-176 1
## LUNG1-177 1
## LUNG1-178 1
## LUNG1-179 1
## LUNG1-180 1
## LUNG1-181 1
## LUNG1-182 1
## LUNG1-183 1
## LUNG1-184 1
## LUNG1-186 1
## LUNG1-187 1
## LUNG1-188 1
## LUNG1-189 1
## LUNG1-190 1
## LUNG1-191 1
## LUNG1-192 1
## LUNG1-193 1
## LUNG1-195 1
## LUNG1-196 1
## LUNG1-197 1
## LUNG1-198 1
## LUNG1-199 1
## LUNG1-200 1
## LUNG1-201 1
## LUNG1-202 1
## LUNG1-203 1
## LUNG1-204 1
## LUNG1-205 1
## LUNG1-206 1
## LUNG1-207 1
## LUNG1-208 1
## LUNG1-209 1
## LUNG1-210 1
## LUNG1-211 1
## LUNG1-212 1
## LUNG1-213 1
## LUNG1-214 1
## LUNG1-215 1
## LUNG1-216 1
## LUNG1-217 1
## LUNG1-218 1
## LUNG1-219 1
## LUNG1-220 1
## LUNG1-221 1
## LUNG1-222 1
## LUNG1-223 1
## LUNG1-224 1
## LUNG1-225 1
## LUNG1-226 1
## LUNG1-227 1
## LUNG1-228 1
## LUNG1-229 1
## LUNG1-230 1
## LUNG1-231 1
## LUNG1-232 1
## LUNG1-233 1
## LUNG1-234 1
## LUNG1-235 1
## LUNG1-236 1
## LUNG1-237 1
## LUNG1-238 1
## LUNG1-239 1
## LUNG1-240 1
## LUNG1-242 1
## LUNG1-243 1
## LUNG1-244 1
## LUNG1-245 1
## LUNG1-247 1
## LUNG1-248 1
## LUNG1-250 1
## LUNG1-251 1
## LUNG1-252 1
## LUNG1-253 1
## LUNG1-254 1
## LUNG1-256 1
## LUNG1-257 1
## LUNG1-258 1
## LUNG1-259 1
## LUNG1-261 1
## LUNG1-262 1
## LUNG1-263 1
## LUNG1-264 1
## LUNG1-265 1
## LUNG1-266 1
## LUNG1-267 1
## LUNG1-268 1
## LUNG1-269 1
## LUNG1-270 1
## LUNG1-271 1
## LUNG1-272 1
## LUNG1-273 1
## LUNG1-274 1
## LUNG1-275 1
## LUNG1-276 1
## LUNG1-277 1
## LUNG1-279 1
## LUNG1-280 1
## LUNG1-281 1
## LUNG1-282 1
## LUNG1-283 1
## LUNG1-284 1
## LUNG1-285 1
## LUNG1-286 1
## LUNG1-288 1
## LUNG1-289 1
## LUNG1-290 1
## LUNG1-291 1
## LUNG1-292 1
## LUNG1-293 1
## LUNG1-294 1
## LUNG1-295 1
## LUNG1-296 1
## LUNG1-297 1
## LUNG1-298 1
## LUNG1-299 1
## LUNG1-300 1
## LUNG1-301 1
## LUNG1-302 1
## LUNG1-303 1
## LUNG1-304 1
## LUNG1-307 1
## LUNG1-308 1
## LUNG1-310 1
## LUNG1-311 1
## LUNG1-312 1
## LUNG1-313 1
## LUNG1-314 1
## LUNG1-316 1
## LUNG1-317 1
## LUNG1-318 1
## LUNG1-319 1
## LUNG1-320 1
## LUNG1-321 1
## LUNG1-322 1
## LUNG1-323 1
## LUNG1-324 1
## LUNG1-325 1
## LUNG1-326 1
## LUNG1-327 1
## LUNG1-329 1
## LUNG1-330 1
## LUNG1-331 1
## LUNG1-333 1
## LUNG1-334 1
## LUNG1-335 1
## LUNG1-336 1
## LUNG1-337 1
## LUNG1-339 1
## LUNG1-340 1
## LUNG1-341 1
## LUNG1-342 1
## LUNG1-343 1
## LUNG1-344 1
## LUNG1-345 1
## LUNG1-346 1
## LUNG1-347 1
## LUNG1-348 1
## LUNG1-349 1
## LUNG1-350 1
## LUNG1-351 1
## LUNG1-352 1
## LUNG1-354 1
## LUNG1-355 1
## LUNG1-356 1
## LUNG1-357 1
## LUNG1-358 1
## LUNG1-359 1
## LUNG1-360 1
## LUNG1-361 1
## LUNG1-362 1
## LUNG1-363 1
## LUNG1-364 1
## LUNG1-365 1
## LUNG1-366 1
## LUNG1-367 1
## LUNG1-368 1
## LUNG1-369 1
## LUNG1-370 1
## LUNG1-371 1
## LUNG1-372 1
## LUNG1-373 1
## LUNG1-374 1
## LUNG1-375 1
## LUNG1-376 1
## LUNG1-377 1
## LUNG1-378 1
## LUNG1-379 1
## LUNG1-380 1
## LUNG1-381 1
## LUNG1-382 1
## LUNG1-383 1
## LUNG1-384 1
## LUNG1-385 1
## LUNG1-386 1
## LUNG1-387 1
## LUNG1-388 1
## LUNG1-389 1
## LUNG1-390 1
## LUNG1-391 1
## LUNG1-392 1
## LUNG1-393 1
## LUNG1-395 1
## LUNG1-396 1
## LUNG1-397 1
## LUNG1-398 1
## LUNG1-399 1
## LUNG1-400 1
## LUNG1-401 1
## LUNG1-403 1
## LUNG1-404 1
## LUNG1-405 1
## LUNG1-406 1
## LUNG1-407 1
## LUNG1-408 1
## LUNG1-409 1
## LUNG1-410 1
## LUNG1-411 1
## LUNG1-412 1
## LUNG1-413 1
## LUNG1-414 1
## LUNG1-415 1
## LUNG1-416 1
## LUNG1-417 1
## LUNG1-418 1
## LUNG1-419 1
## LUNG1-420 1
## LUNG1-421 1
## log.sigma.2.0.mm.3D_glcm_Autocorrelation
## LUNG1-001 1
## LUNG1-002 1
## LUNG1-003 1
## LUNG1-004 1
## LUNG1-005 1
## LUNG1-006 1
## LUNG1-007 1
## LUNG1-008 1
## LUNG1-009 1
## LUNG1-010 1
## LUNG1-011 1
## LUNG1-012 1
## LUNG1-013 1
## LUNG1-014 1
## LUNG1-015 1
## LUNG1-016 1
## LUNG1-017 1
## LUNG1-018 1
## LUNG1-019 1
## LUNG1-022 1
## LUNG1-024 1
## LUNG1-025 1
## LUNG1-026 1
## LUNG1-029 1
## LUNG1-030 1
## LUNG1-031 1
## LUNG1-032 1
## LUNG1-033 1
## LUNG1-034 1
## LUNG1-035 1
## LUNG1-036 1
## LUNG1-037 1
## LUNG1-038 1
## LUNG1-040 1
## LUNG1-041 1
## LUNG1-042 1
## LUNG1-043 1
## LUNG1-045 1
## LUNG1-046 1
## LUNG1-047 1
## LUNG1-048 1
## LUNG1-049 1
## LUNG1-050 1
## LUNG1-051 1
## LUNG1-052 1
## LUNG1-053 1
## LUNG1-054 1
## LUNG1-055 1
## LUNG1-057 1
## LUNG1-058 1
## LUNG1-059 1
## LUNG1-060 1
## LUNG1-061 1
## LUNG1-062 1
## LUNG1-063 1
## LUNG1-064 1
## LUNG1-065 1
## LUNG1-067 1
## LUNG1-068 1
## LUNG1-070 1
## LUNG1-071 1
## LUNG1-072 1
## LUNG1-073 1
## LUNG1-074 1
## LUNG1-075 1
## LUNG1-076 1
## LUNG1-077 1
## LUNG1-078 1
## LUNG1-079 1
## LUNG1-081 1
## LUNG1-082 1
## LUNG1-084 1
## LUNG1-085 1
## LUNG1-086 1
## LUNG1-088 1
## LUNG1-089 1
## LUNG1-091 1
## LUNG1-092 1
## LUNG1-093 1
## LUNG1-095 1
## LUNG1-096 1
## LUNG1-097 1
## LUNG1-098 1
## LUNG1-099 1
## LUNG1-101 1
## LUNG1-102 1
## LUNG1-103 1
## LUNG1-104 1
## LUNG1-105 1
## LUNG1-106 1
## LUNG1-107 1
## LUNG1-108 1
## LUNG1-109 1
## LUNG1-110 1
## LUNG1-112 1
## LUNG1-113 1
## LUNG1-114 1
## LUNG1-115 1
## LUNG1-116 1
## LUNG1-117 1
## LUNG1-118 1
## LUNG1-119 1
## LUNG1-120 1
## LUNG1-121 1
## LUNG1-122 1
## LUNG1-123 1
## LUNG1-124 1
## LUNG1-125 1
## LUNG1-126 1
## LUNG1-127 1
## LUNG1-129 1
## LUNG1-130 1
## LUNG1-131 1
## LUNG1-132 1
## LUNG1-134 1
## LUNG1-135 1
## LUNG1-137 1
## LUNG1-139 1
## LUNG1-140 1
## LUNG1-141 1
## LUNG1-142 1
## LUNG1-143 1
## LUNG1-144 1
## LUNG1-145 1
## LUNG1-146 1
## LUNG1-147 1
## LUNG1-148 1
## LUNG1-149 1
## LUNG1-150 1
## LUNG1-151 1
## LUNG1-152 1
## LUNG1-153 1
## LUNG1-155 1
## LUNG1-156 1
## LUNG1-158 1
## LUNG1-159 1
## LUNG1-160 1
## LUNG1-162 1
## LUNG1-163 1
## LUNG1-164 1
## LUNG1-165 1
## LUNG1-166 1
## LUNG1-167 1
## LUNG1-168 1
## LUNG1-169 1
## LUNG1-170 1
## LUNG1-172 1
## LUNG1-173 1
## LUNG1-174 1
## LUNG1-175 1
## LUNG1-176 1
## LUNG1-177 1
## LUNG1-178 1
## LUNG1-179 1
## LUNG1-180 1
## LUNG1-181 1
## LUNG1-182 1
## LUNG1-183 1
## LUNG1-184 1
## LUNG1-186 1
## LUNG1-187 1
## LUNG1-188 1
## LUNG1-189 1
## LUNG1-190 1
## LUNG1-191 1
## LUNG1-192 1
## LUNG1-193 1
## LUNG1-195 1
## LUNG1-196 1
## LUNG1-197 1
## LUNG1-198 1
## LUNG1-199 1
## LUNG1-200 1
## LUNG1-201 1
## LUNG1-202 1
## LUNG1-203 1
## LUNG1-204 1
## LUNG1-205 1
## LUNG1-206 1
## LUNG1-207 1
## LUNG1-208 1
## LUNG1-209 1
## LUNG1-210 1
## LUNG1-211 1
## LUNG1-212 1
## LUNG1-213 1
## LUNG1-214 1
## LUNG1-215 1
## LUNG1-216 1
## LUNG1-217 1
## LUNG1-218 1
## LUNG1-219 1
## LUNG1-220 1
## LUNG1-221 1
## LUNG1-222 1
## LUNG1-223 1
## LUNG1-224 1
## LUNG1-225 1
## LUNG1-226 1
## LUNG1-227 1
## LUNG1-228 1
## LUNG1-229 1
## LUNG1-230 1
## LUNG1-231 1
## LUNG1-232 1
## LUNG1-233 1
## LUNG1-234 1
## LUNG1-235 1
## LUNG1-236 1
## LUNG1-237 1
## LUNG1-238 1
## LUNG1-239 1
## LUNG1-240 1
## LUNG1-242 1
## LUNG1-243 1
## LUNG1-244 1
## LUNG1-245 1
## LUNG1-247 1
## LUNG1-248 1
## LUNG1-250 1
## LUNG1-251 1
## LUNG1-252 1
## LUNG1-253 1
## LUNG1-254 1
## LUNG1-256 1
## LUNG1-257 1
## LUNG1-258 1
## LUNG1-259 1
## LUNG1-261 1
## LUNG1-262 1
## LUNG1-263 1
## LUNG1-264 1
## LUNG1-265 1
## LUNG1-266 1
## LUNG1-267 1
## LUNG1-268 1
## LUNG1-269 1
## LUNG1-270 1
## LUNG1-271 1
## LUNG1-272 1
## LUNG1-273 1
## LUNG1-274 1
## LUNG1-275 1
## LUNG1-276 1
## LUNG1-277 1
## LUNG1-279 1
## LUNG1-280 1
## LUNG1-281 1
## LUNG1-282 1
## LUNG1-283 1
## LUNG1-284 1
## LUNG1-285 1
## LUNG1-286 1
## LUNG1-288 1
## LUNG1-289 1
## LUNG1-290 1
## LUNG1-291 1
## LUNG1-292 1
## LUNG1-293 1
## LUNG1-294 1
## LUNG1-295 1
## LUNG1-296 1
## LUNG1-297 1
## LUNG1-298 1
## LUNG1-299 1
## LUNG1-300 1
## LUNG1-301 1
## LUNG1-302 1
## LUNG1-303 1
## LUNG1-304 1
## LUNG1-307 1
## LUNG1-308 1
## LUNG1-310 1
## LUNG1-311 1
## LUNG1-312 1
## LUNG1-313 1
## LUNG1-314 1
## LUNG1-316 1
## LUNG1-317 1
## LUNG1-318 1
## LUNG1-319 1
## LUNG1-320 1
## LUNG1-321 1
## LUNG1-322 1
## LUNG1-323 1
## LUNG1-324 1
## LUNG1-325 1
## LUNG1-326 1
## LUNG1-327 1
## LUNG1-329 1
## LUNG1-330 1
## LUNG1-331 1
## LUNG1-333 1
## LUNG1-334 1
## LUNG1-335 1
## LUNG1-336 1
## LUNG1-337 1
## LUNG1-339 1
## LUNG1-340 1
## LUNG1-341 1
## LUNG1-342 1
## LUNG1-343 1
## LUNG1-344 1
## LUNG1-345 1
## LUNG1-346 1
## LUNG1-347 1
## LUNG1-348 1
## LUNG1-349 1
## LUNG1-350 1
## LUNG1-351 1
## LUNG1-352 1
## LUNG1-354 1
## LUNG1-355 1
## LUNG1-356 1
## LUNG1-357 1
## LUNG1-358 1
## LUNG1-359 1
## LUNG1-360 1
## LUNG1-361 1
## LUNG1-362 1
## LUNG1-363 1
## LUNG1-364 1
## LUNG1-365 1
## LUNG1-366 1
## LUNG1-367 1
## LUNG1-368 1
## LUNG1-369 1
## LUNG1-370 1
## LUNG1-371 1
## LUNG1-372 1
## LUNG1-373 1
## LUNG1-374 1
## LUNG1-375 1
## LUNG1-376 1
## LUNG1-377 1
## LUNG1-378 1
## LUNG1-379 1
## LUNG1-380 1
## LUNG1-381 1
## LUNG1-382 1
## LUNG1-383 1
## LUNG1-384 1
## LUNG1-385 1
## LUNG1-386 1
## LUNG1-387 1
## LUNG1-388 1
## LUNG1-389 1
## LUNG1-390 1
## LUNG1-391 1
## LUNG1-392 1
## LUNG1-393 1
## LUNG1-395 1
## LUNG1-396 1
## LUNG1-397 1
## LUNG1-398 1
## LUNG1-399 1
## LUNG1-400 1
## LUNG1-401 1
## LUNG1-403 1
## LUNG1-404 1
## LUNG1-405 1
## LUNG1-406 1
## LUNG1-407 1
## LUNG1-408 1
## LUNG1-409 1
## LUNG1-410 1
## LUNG1-411 1
## LUNG1-412 1
## LUNG1-413 1
## LUNG1-414 1
## LUNG1-415 1
## LUNG1-416 1
## LUNG1-417 1
## LUNG1-418 1
## LUNG1-419 1
## LUNG1-420 1
## LUNG1-421 1
## log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis
## LUNG1-001 1
## LUNG1-002 1
## LUNG1-003 1
## LUNG1-004 1
## LUNG1-005 1
## LUNG1-006 1
## LUNG1-007 1
## LUNG1-008 1
## LUNG1-009 1
## LUNG1-010 1
## LUNG1-011 1
## LUNG1-012 1
## LUNG1-013 1
## LUNG1-014 1
## LUNG1-015 1
## LUNG1-016 1
## LUNG1-017 1
## LUNG1-018 1
## LUNG1-019 1
## LUNG1-022 1
## LUNG1-024 1
## LUNG1-025 1
## LUNG1-026 1
## LUNG1-029 1
## LUNG1-030 1
## LUNG1-031 1
## LUNG1-032 1
## LUNG1-033 1
## LUNG1-034 1
## LUNG1-035 1
## LUNG1-036 1
## LUNG1-037 1
## LUNG1-038 1
## LUNG1-040 1
## LUNG1-041 1
## LUNG1-042 1
## LUNG1-043 1
## LUNG1-045 1
## LUNG1-046 1
## LUNG1-047 1
## LUNG1-048 1
## LUNG1-049 1
## LUNG1-050 1
## LUNG1-051 1
## LUNG1-052 1
## LUNG1-053 1
## LUNG1-054 1
## LUNG1-055 1
## LUNG1-057 1
## LUNG1-058 1
## LUNG1-059 1
## LUNG1-060 1
## LUNG1-061 1
## LUNG1-062 1
## LUNG1-063 1
## LUNG1-064 1
## LUNG1-065 1
## LUNG1-067 1
## LUNG1-068 1
## LUNG1-070 1
## LUNG1-071 1
## LUNG1-072 1
## LUNG1-073 1
## LUNG1-074 1
## LUNG1-075 1
## LUNG1-076 1
## LUNG1-077 1
## LUNG1-078 1
## LUNG1-079 1
## LUNG1-081 1
## LUNG1-082 1
## LUNG1-084 1
## LUNG1-085 1
## LUNG1-086 1
## LUNG1-088 1
## LUNG1-089 1
## LUNG1-091 1
## LUNG1-092 1
## LUNG1-093 1
## LUNG1-095 1
## LUNG1-096 1
## LUNG1-097 1
## LUNG1-098 1
## LUNG1-099 1
## LUNG1-101 1
## LUNG1-102 1
## LUNG1-103 1
## LUNG1-104 1
## LUNG1-105 1
## LUNG1-106 1
## LUNG1-107 1
## LUNG1-108 1
## LUNG1-109 1
## LUNG1-110 1
## LUNG1-112 1
## LUNG1-113 1
## LUNG1-114 1
## LUNG1-115 1
## LUNG1-116 1
## LUNG1-117 1
## LUNG1-118 1
## LUNG1-119 1
## LUNG1-120 1
## LUNG1-121 1
## LUNG1-122 1
## LUNG1-123 1
## LUNG1-124 1
## LUNG1-125 1
## LUNG1-126 1
## LUNG1-127 1
## LUNG1-129 1
## LUNG1-130 1
## LUNG1-131 1
## LUNG1-132 1
## LUNG1-134 1
## LUNG1-135 1
## LUNG1-137 1
## LUNG1-139 1
## LUNG1-140 1
## LUNG1-141 1
## LUNG1-142 1
## LUNG1-143 1
## LUNG1-144 1
## LUNG1-145 1
## LUNG1-146 1
## LUNG1-147 1
## LUNG1-148 1
## LUNG1-149 1
## LUNG1-150 1
## LUNG1-151 1
## LUNG1-152 1
## LUNG1-153 1
## LUNG1-155 1
## LUNG1-156 1
## LUNG1-158 1
## LUNG1-159 1
## LUNG1-160 1
## LUNG1-162 1
## LUNG1-163 1
## LUNG1-164 1
## LUNG1-165 1
## LUNG1-166 1
## LUNG1-167 1
## LUNG1-168 1
## LUNG1-169 1
## LUNG1-170 1
## LUNG1-172 1
## LUNG1-173 1
## LUNG1-174 1
## LUNG1-175 1
## LUNG1-176 1
## LUNG1-177 1
## LUNG1-178 1
## LUNG1-179 1
## LUNG1-180 1
## LUNG1-181 1
## LUNG1-182 1
## LUNG1-183 1
## LUNG1-184 1
## LUNG1-186 1
## LUNG1-187 1
## LUNG1-188 1
## LUNG1-189 1
## LUNG1-190 1
## LUNG1-191 1
## LUNG1-192 1
## LUNG1-193 1
## LUNG1-195 1
## LUNG1-196 1
## LUNG1-197 1
## LUNG1-198 1
## LUNG1-199 1
## LUNG1-200 1
## LUNG1-201 1
## LUNG1-202 1
## LUNG1-203 1
## LUNG1-204 1
## LUNG1-205 1
## LUNG1-206 1
## LUNG1-207 1
## LUNG1-208 1
## LUNG1-209 1
## LUNG1-210 1
## LUNG1-211 1
## LUNG1-212 1
## LUNG1-213 1
## LUNG1-214 1
## LUNG1-215 1
## LUNG1-216 1
## LUNG1-217 1
## LUNG1-218 1
## LUNG1-219 1
## LUNG1-220 1
## LUNG1-221 1
## LUNG1-222 1
## LUNG1-223 1
## LUNG1-224 1
## LUNG1-225 1
## LUNG1-226 1
## LUNG1-227 1
## LUNG1-228 1
## LUNG1-229 1
## LUNG1-230 1
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## LUNG1-143 1
## LUNG1-144 1
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## LUNG1-146 1
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## LUNG1-148 1
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## LUNG1-150 1
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## LUNG1-152 1
## LUNG1-153 1
## LUNG1-155 1
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## LUNG1-159 1
## LUNG1-160 1
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## LUNG1-172 1
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## LUNG1-180 1
## LUNG1-181 1
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## LUNG1-183 1
## LUNG1-184 1
## LUNG1-186 1
## LUNG1-187 1
## LUNG1-188 1
## LUNG1-189 1
## LUNG1-190 1
## LUNG1-191 1
## LUNG1-192 1
## LUNG1-193 1
## LUNG1-195 1
## LUNG1-196 1
## LUNG1-197 1
## LUNG1-198 1
## LUNG1-199 1
## LUNG1-200 1
## LUNG1-201 1
## LUNG1-202 1
## LUNG1-203 1
## LUNG1-204 1
## LUNG1-205 1
## LUNG1-206 1
## LUNG1-207 1
## LUNG1-208 1
## LUNG1-209 1
## LUNG1-210 1
## LUNG1-211 1
## LUNG1-212 1
## LUNG1-213 1
## LUNG1-214 1
## LUNG1-215 1
## LUNG1-216 1
## LUNG1-217 1
## LUNG1-218 1
## LUNG1-219 1
## LUNG1-220 1
## LUNG1-221 1
## LUNG1-222 1
## LUNG1-223 1
## LUNG1-224 1
## LUNG1-225 1
## LUNG1-226 1
## LUNG1-227 1
## LUNG1-228 1
## LUNG1-229 1
## LUNG1-230 1
## LUNG1-231 1
## LUNG1-232 1
## LUNG1-233 1
## LUNG1-234 1
## LUNG1-235 1
## LUNG1-236 1
## LUNG1-237 1
## LUNG1-238 1
## LUNG1-239 1
## LUNG1-240 1
## LUNG1-242 1
## LUNG1-243 1
## LUNG1-244 1
## LUNG1-245 1
## LUNG1-247 1
## LUNG1-248 1
## LUNG1-250 1
## LUNG1-251 1
## LUNG1-252 1
## LUNG1-253 1
## LUNG1-254 1
## LUNG1-256 1
## LUNG1-257 1
## LUNG1-258 1
## LUNG1-259 1
## LUNG1-261 1
## LUNG1-262 1
## LUNG1-263 1
## LUNG1-264 1
## LUNG1-265 1
## LUNG1-266 1
## LUNG1-267 1
## LUNG1-268 1
## LUNG1-269 1
## LUNG1-270 1
## LUNG1-271 1
## LUNG1-272 1
## LUNG1-273 1
## LUNG1-274 1
## LUNG1-275 1
## LUNG1-276 1
## LUNG1-277 1
## LUNG1-279 1
## LUNG1-280 1
## LUNG1-281 1
## LUNG1-282 1
## LUNG1-283 1
## LUNG1-284 1
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## LUNG1-286 1
## LUNG1-288 1
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## LUNG1-294 1
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## LUNG1-300 1
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## LUNG1-307 1
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## LUNG1-310 1
## LUNG1-311 1
## LUNG1-312 1
## LUNG1-313 1
## LUNG1-314 1
## LUNG1-316 1
## LUNG1-317 1
## LUNG1-318 1
## LUNG1-319 1
## LUNG1-320 1
## LUNG1-321 1
## LUNG1-322 1
## LUNG1-323 1
## LUNG1-324 1
## LUNG1-325 1
## LUNG1-326 1
## LUNG1-327 1
## LUNG1-329 1
## LUNG1-330 1
## LUNG1-331 1
## LUNG1-333 1
## LUNG1-334 1
## LUNG1-335 1
## LUNG1-336 1
## LUNG1-337 1
## LUNG1-339 1
## LUNG1-340 1
## LUNG1-341 1
## LUNG1-342 1
## LUNG1-343 1
## LUNG1-344 1
## LUNG1-345 1
## LUNG1-346 1
## LUNG1-347 1
## LUNG1-348 1
## LUNG1-349 1
## LUNG1-350 1
## LUNG1-351 1
## LUNG1-352 1
## LUNG1-354 1
## LUNG1-355 1
## LUNG1-356 1
## LUNG1-357 1
## LUNG1-358 1
## LUNG1-359 1
## LUNG1-360 1
## LUNG1-361 1
## LUNG1-362 1
## LUNG1-363 1
## LUNG1-364 1
## LUNG1-365 1
## LUNG1-366 1
## LUNG1-367 1
## LUNG1-368 1
## LUNG1-369 1
## LUNG1-370 1
## LUNG1-371 1
## LUNG1-372 1
## LUNG1-373 1
## LUNG1-374 1
## LUNG1-375 1
## LUNG1-376 1
## LUNG1-377 1
## LUNG1-378 1
## LUNG1-379 1
## LUNG1-380 1
## LUNG1-381 1
## LUNG1-382 1
## LUNG1-383 1
## LUNG1-384 1
## LUNG1-385 1
## LUNG1-386 1
## LUNG1-387 1
## LUNG1-388 1
## LUNG1-389 1
## LUNG1-390 1
## LUNG1-391 1
## LUNG1-392 1
## LUNG1-393 1
## LUNG1-395 1
## LUNG1-396 1
## LUNG1-397 1
## LUNG1-398 1
## LUNG1-399 1
## LUNG1-400 1
## LUNG1-401 1
## LUNG1-403 1
## LUNG1-404 1
## LUNG1-405 1
## LUNG1-406 1
## LUNG1-407 1
## LUNG1-408 1
## LUNG1-409 1
## LUNG1-410 1
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## LUNG1-413 1
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## LUNG1-415 1
## LUNG1-416 1
## LUNG1-417 1
## LUNG1-418 1
## LUNG1-419 1
## LUNG1-420 1
## LUNG1-421 1
## wavelet.HHH_glrlm_GrayLevelVariance
## LUNG1-001 1
## LUNG1-002 1
## LUNG1-003 1
## LUNG1-004 1
## LUNG1-005 1
## LUNG1-006 1
## LUNG1-007 1
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## LUNG1-010 1
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## LUNG1-012 1
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## LUNG1-015 1
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## LUNG1-017 1
## LUNG1-018 1
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## LUNG1-025 1
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## LUNG1-035 1
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## LUNG1-105 1
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## LUNG1-112 1
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## LUNG1-140 1
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## LUNG1-143 1
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## LUNG1-145 1
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## LUNG1-147 1
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## LUNG1-149 1
## LUNG1-150 1
## LUNG1-151 1
## LUNG1-152 1
## LUNG1-153 1
## LUNG1-155 1
## LUNG1-156 1
## LUNG1-158 1
## LUNG1-159 1
## LUNG1-160 1
## LUNG1-162 1
## LUNG1-163 1
## LUNG1-164 1
## LUNG1-165 1
## LUNG1-166 1
## LUNG1-167 1
## LUNG1-168 1
## LUNG1-169 1
## LUNG1-170 1
## LUNG1-172 1
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## LUNG1-180 1
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## LUNG1-183 1
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## LUNG1-186 1
## LUNG1-187 1
## LUNG1-188 1
## LUNG1-189 1
## LUNG1-190 1
## LUNG1-191 1
## LUNG1-192 1
## LUNG1-193 1
## LUNG1-195 1
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## LUNG1-198 1
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## LUNG1-200 1
## LUNG1-201 1
## LUNG1-202 1
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## LUNG1-204 1
## LUNG1-205 1
## LUNG1-206 1
## LUNG1-207 1
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## LUNG1-209 1
## LUNG1-210 1
## LUNG1-211 1
## LUNG1-212 1
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## LUNG1-214 1
## LUNG1-215 1
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## LUNG1-217 1
## LUNG1-218 1
## LUNG1-219 1
## LUNG1-220 1
## LUNG1-221 1
## LUNG1-222 1
## LUNG1-223 1
## LUNG1-224 1
## LUNG1-225 1
## LUNG1-226 1
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## LUNG1-228 1
## LUNG1-229 1
## LUNG1-230 1
## LUNG1-231 1
## LUNG1-232 1
## LUNG1-233 1
## LUNG1-234 1
## LUNG1-235 1
## LUNG1-236 1
## LUNG1-237 1
## LUNG1-238 1
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## LUNG1-240 1
## LUNG1-242 1
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## LUNG1-244 1
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## LUNG1-248 1
## LUNG1-250 1
## LUNG1-251 1
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## LUNG1-254 1
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## LUNG1-257 1
## LUNG1-258 1
## LUNG1-259 1
## LUNG1-261 1
## LUNG1-262 1
## LUNG1-263 1
## LUNG1-264 1
## LUNG1-265 1
## LUNG1-266 1
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## LUNG1-268 1
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## LUNG1-270 1
## LUNG1-271 1
## LUNG1-272 1
## LUNG1-273 1
## LUNG1-274 1
## LUNG1-275 1
## LUNG1-276 1
## LUNG1-277 1
## LUNG1-279 1
## LUNG1-280 1
## LUNG1-281 1
## LUNG1-282 1
## LUNG1-283 1
## LUNG1-284 1
## LUNG1-285 1
## LUNG1-286 1
## LUNG1-288 1
## LUNG1-289 1
## LUNG1-290 1
## LUNG1-291 1
## LUNG1-292 1
## LUNG1-293 1
## LUNG1-294 1
## LUNG1-295 1
## LUNG1-296 1
## LUNG1-297 1
## LUNG1-298 1
## LUNG1-299 1
## LUNG1-300 1
## LUNG1-301 1
## LUNG1-302 1
## LUNG1-303 1
## LUNG1-304 1
## LUNG1-307 1
## LUNG1-308 1
## LUNG1-310 1
## LUNG1-311 1
## LUNG1-312 1
## LUNG1-313 1
## LUNG1-314 1
## LUNG1-316 1
## LUNG1-317 1
## LUNG1-318 1
## LUNG1-319 1
## LUNG1-320 1
## LUNG1-321 1
## LUNG1-322 1
## LUNG1-323 1
## LUNG1-324 1
## LUNG1-325 1
## LUNG1-326 1
## LUNG1-327 1
## LUNG1-329 1
## LUNG1-330 1
## LUNG1-331 1
## LUNG1-333 1
## LUNG1-334 1
## LUNG1-335 1
## LUNG1-336 1
## LUNG1-337 1
## LUNG1-339 1
## LUNG1-340 1
## LUNG1-341 1
## LUNG1-342 1
## LUNG1-343 1
## LUNG1-344 1
## LUNG1-345 1
## LUNG1-346 1
## LUNG1-347 1
## LUNG1-348 1
## LUNG1-349 1
## LUNG1-350 1
## LUNG1-351 1
## LUNG1-352 1
## LUNG1-354 1
## LUNG1-355 1
## LUNG1-356 1
## LUNG1-357 1
## LUNG1-358 1
## LUNG1-359 1
## LUNG1-360 1
## LUNG1-361 1
## LUNG1-362 1
## LUNG1-363 1
## LUNG1-364 1
## LUNG1-365 1
## LUNG1-366 1
## LUNG1-367 1
## LUNG1-368 1
## LUNG1-369 1
## LUNG1-370 1
## LUNG1-371 1
## LUNG1-372 1
## LUNG1-373 1
## LUNG1-374 1
## LUNG1-375 1
## LUNG1-376 1
## LUNG1-377 1
## LUNG1-378 1
## LUNG1-379 1
## LUNG1-380 1
## LUNG1-381 1
## LUNG1-382 1
## LUNG1-383 1
## LUNG1-384 1
## LUNG1-385 1
## LUNG1-386 1
## LUNG1-387 1
## LUNG1-388 1
## LUNG1-389 1
## LUNG1-390 1
## LUNG1-391 1
## LUNG1-392 1
## LUNG1-393 1
## LUNG1-395 1
## LUNG1-396 1
## LUNG1-397 1
## LUNG1-398 1
## LUNG1-399 1
## LUNG1-400 1
## LUNG1-401 1
## LUNG1-403 1
## LUNG1-404 1
## LUNG1-405 1
## LUNG1-406 1
## LUNG1-407 1
## LUNG1-408 1
## LUNG1-409 1
## LUNG1-410 1
## LUNG1-411 1
## LUNG1-412 1
## LUNG1-413 1
## LUNG1-414 1
## LUNG1-415 1
## LUNG1-416 1
## LUNG1-417 1
## LUNG1-418 1
## LUNG1-419 1
## LUNG1-420 1
## LUNG1-421 1
## log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized
## LUNG1-001 1
## LUNG1-002 1
## LUNG1-003 1
## LUNG1-004 1
## LUNG1-005 1
## LUNG1-006 1
## LUNG1-007 1
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## LUNG1-009 1
## LUNG1-010 1
## LUNG1-011 1
## LUNG1-012 1
## LUNG1-013 1
## LUNG1-014 1
## LUNG1-015 1
## LUNG1-016 1
## LUNG1-017 1
## LUNG1-018 1
## LUNG1-019 1
## LUNG1-022 1
## LUNG1-024 1
## LUNG1-025 1
## LUNG1-026 1
## LUNG1-029 1
## LUNG1-030 1
## LUNG1-031 1
## LUNG1-032 1
## LUNG1-033 1
## LUNG1-034 1
## LUNG1-035 1
## LUNG1-036 1
## LUNG1-037 1
## LUNG1-038 1
## LUNG1-040 1
## LUNG1-041 1
## LUNG1-042 1
## LUNG1-043 1
## LUNG1-045 1
## LUNG1-046 1
## LUNG1-047 1
## LUNG1-048 1
## LUNG1-049 1
## LUNG1-050 1
## LUNG1-051 1
## LUNG1-052 1
## LUNG1-053 1
## LUNG1-054 1
## LUNG1-055 1
## LUNG1-057 1
## LUNG1-058 1
## LUNG1-059 1
## LUNG1-060 1
## LUNG1-061 1
## LUNG1-062 1
## LUNG1-063 1
## LUNG1-064 1
## LUNG1-065 1
## LUNG1-067 1
## LUNG1-068 1
## LUNG1-070 1
## LUNG1-071 1
## LUNG1-072 1
## LUNG1-073 1
## LUNG1-074 1
## LUNG1-075 1
## LUNG1-076 1
## LUNG1-077 1
## LUNG1-078 1
## LUNG1-079 1
## LUNG1-081 1
## LUNG1-082 1
## LUNG1-084 1
## LUNG1-085 1
## LUNG1-086 1
## LUNG1-088 1
## LUNG1-089 1
## LUNG1-091 1
## LUNG1-092 1
## LUNG1-093 1
## LUNG1-095 1
## LUNG1-096 1
## LUNG1-097 1
## LUNG1-098 1
## LUNG1-099 1
## LUNG1-101 1
## LUNG1-102 1
## LUNG1-103 1
## LUNG1-104 1
## LUNG1-105 1
## LUNG1-106 1
## LUNG1-107 1
## LUNG1-108 1
## LUNG1-109 1
## LUNG1-110 1
## LUNG1-112 1
## LUNG1-113 1
## LUNG1-114 1
## LUNG1-115 1
## LUNG1-116 1
## LUNG1-117 1
## LUNG1-118 1
## LUNG1-119 1
## LUNG1-120 1
## LUNG1-121 1
## LUNG1-122 1
## LUNG1-123 1
## LUNG1-124 1
## LUNG1-125 1
## LUNG1-126 1
## LUNG1-127 1
## LUNG1-129 1
## LUNG1-130 1
## LUNG1-131 1
## LUNG1-132 1
## LUNG1-134 1
## LUNG1-135 1
## LUNG1-137 1
## LUNG1-139 1
## LUNG1-140 1
## LUNG1-141 1
## LUNG1-142 1
## LUNG1-143 1
## LUNG1-144 1
## LUNG1-145 1
## LUNG1-146 1
## LUNG1-147 1
## LUNG1-148 1
## LUNG1-149 1
## LUNG1-150 1
## LUNG1-151 1
## LUNG1-152 1
## LUNG1-153 1
## LUNG1-155 1
## LUNG1-156 1
## LUNG1-158 1
## LUNG1-159 1
## LUNG1-160 1
## LUNG1-162 1
## LUNG1-163 1
## LUNG1-164 1
## LUNG1-165 1
## LUNG1-166 1
## LUNG1-167 1
## LUNG1-168 1
## LUNG1-169 1
## LUNG1-170 1
## LUNG1-172 1
## LUNG1-173 1
## LUNG1-174 1
## LUNG1-175 1
## LUNG1-176 1
## LUNG1-177 1
## LUNG1-178 1
## LUNG1-179 1
## LUNG1-180 1
## LUNG1-181 1
## LUNG1-182 1
## LUNG1-183 1
## LUNG1-184 1
## LUNG1-186 1
## LUNG1-187 1
## LUNG1-188 1
## LUNG1-189 1
## LUNG1-190 1
## LUNG1-191 1
## LUNG1-192 1
## LUNG1-193 1
## LUNG1-195 1
## LUNG1-196 1
## LUNG1-197 1
## LUNG1-198 1
## LUNG1-199 1
## LUNG1-200 1
## LUNG1-201 1
## LUNG1-202 1
## LUNG1-203 1
## LUNG1-204 1
## LUNG1-205 1
## LUNG1-206 1
## LUNG1-207 1
## LUNG1-208 1
## LUNG1-209 1
## LUNG1-210 1
## LUNG1-211 1
## LUNG1-212 1
## LUNG1-213 1
## LUNG1-214 1
## LUNG1-215 1
## LUNG1-216 1
## LUNG1-217 1
## LUNG1-218 1
## LUNG1-219 1
## LUNG1-220 1
## LUNG1-221 1
## LUNG1-222 1
## LUNG1-223 1
## LUNG1-224 1
## LUNG1-225 1
## LUNG1-226 1
## LUNG1-227 1
## LUNG1-228 1
## LUNG1-229 1
## LUNG1-230 1
## LUNG1-231 1
## LUNG1-232 1
## LUNG1-233 1
## LUNG1-234 1
## LUNG1-235 1
## LUNG1-236 1
## LUNG1-237 1
## LUNG1-238 1
## LUNG1-239 1
## LUNG1-240 1
## LUNG1-242 1
## LUNG1-243 1
## LUNG1-244 1
## LUNG1-245 1
## LUNG1-247 1
## LUNG1-248 1
## LUNG1-250 1
## LUNG1-251 1
## LUNG1-252 1
## LUNG1-253 1
## LUNG1-254 1
## LUNG1-256 1
## LUNG1-257 1
## LUNG1-258 1
## LUNG1-259 1
## LUNG1-261 1
## LUNG1-262 1
## LUNG1-263 1
## LUNG1-264 1
## LUNG1-265 1
## LUNG1-266 1
## LUNG1-267 1
## LUNG1-268 1
## LUNG1-269 1
## LUNG1-270 1
## LUNG1-271 1
## LUNG1-272 1
## LUNG1-273 1
## LUNG1-274 1
## LUNG1-275 1
## LUNG1-276 1
## LUNG1-277 1
## LUNG1-279 1
## LUNG1-280 1
## LUNG1-281 1
## LUNG1-282 1
## LUNG1-283 1
## LUNG1-284 1
## LUNG1-285 1
## LUNG1-286 1
## LUNG1-288 1
## LUNG1-289 1
## LUNG1-290 1
## LUNG1-291 1
## LUNG1-292 1
## LUNG1-293 1
## LUNG1-294 1
## LUNG1-295 1
## LUNG1-296 1
## LUNG1-297 1
## LUNG1-298 1
## LUNG1-299 1
## LUNG1-300 1
## LUNG1-301 1
## LUNG1-302 1
## LUNG1-303 1
## LUNG1-304 1
## LUNG1-307 1
## LUNG1-308 1
## LUNG1-310 1
## LUNG1-311 1
## LUNG1-312 1
## LUNG1-313 1
## LUNG1-314 1
## LUNG1-316 1
## LUNG1-317 1
## LUNG1-318 1
## LUNG1-319 1
## LUNG1-320 1
## LUNG1-321 1
## LUNG1-322 1
## LUNG1-323 1
## LUNG1-324 1
## LUNG1-325 1
## LUNG1-326 1
## LUNG1-327 1
## LUNG1-329 1
## LUNG1-330 1
## LUNG1-331 1
## LUNG1-333 1
## LUNG1-334 1
## LUNG1-335 1
## LUNG1-336 1
## LUNG1-337 1
## LUNG1-339 1
## LUNG1-340 1
## LUNG1-341 1
## LUNG1-342 1
## LUNG1-343 1
## LUNG1-344 1
## LUNG1-345 1
## LUNG1-346 1
## LUNG1-347 1
## LUNG1-348 1
## LUNG1-349 1
## LUNG1-350 1
## LUNG1-351 1
## LUNG1-352 1
## LUNG1-354 1
## LUNG1-355 1
## LUNG1-356 1
## LUNG1-357 1
## LUNG1-358 1
## LUNG1-359 1
## LUNG1-360 1
## LUNG1-361 1
## LUNG1-362 1
## LUNG1-363 1
## LUNG1-364 1
## LUNG1-365 1
## LUNG1-366 1
## LUNG1-367 1
## LUNG1-368 1
## LUNG1-369 1
## LUNG1-370 1
## LUNG1-371 1
## LUNG1-372 1
## LUNG1-373 1
## LUNG1-374 1
## LUNG1-375 1
## LUNG1-376 1
## LUNG1-377 1
## LUNG1-378 1
## LUNG1-379 1
## LUNG1-380 1
## LUNG1-381 1
## LUNG1-382 1
## LUNG1-383 1
## LUNG1-384 1
## LUNG1-385 1
## LUNG1-386 1
## LUNG1-387 1
## LUNG1-388 1
## LUNG1-389 1
## LUNG1-390 1
## LUNG1-391 1
## LUNG1-392 1
## LUNG1-393 1
## LUNG1-395 1
## LUNG1-396 1
## LUNG1-397 1
## LUNG1-398 1
## LUNG1-399 1
## LUNG1-400 1
## LUNG1-401 1
## LUNG1-403 1
## LUNG1-404 1
## LUNG1-405 1
## LUNG1-406 1
## LUNG1-407 1
## LUNG1-408 1
## LUNG1-409 1
## LUNG1-410 1
## LUNG1-411 1
## LUNG1-412 1
## LUNG1-413 1
## LUNG1-414 1
## LUNG1-415 1
## LUNG1-416 1
## LUNG1-417 1
## LUNG1-418 1
## LUNG1-419 1
## LUNG1-420 1
## LUNG1-421 1
## wavelet.HHH_glcm_Imc1 original_glcm_Imc1
## LUNG1-001 1 1
## LUNG1-002 1 1
## LUNG1-003 1 1
## LUNG1-004 1 1
## LUNG1-005 1 1
## LUNG1-006 1 1
## LUNG1-007 1 1
## LUNG1-008 1 1
## LUNG1-009 1 1
## LUNG1-010 1 1
## LUNG1-011 1 1
## LUNG1-012 1 1
## LUNG1-013 1 1
## LUNG1-014 1 1
## LUNG1-015 1 1
## LUNG1-016 1 1
## LUNG1-017 1 1
## LUNG1-018 1 1
## LUNG1-019 1 1
## LUNG1-022 1 1
## LUNG1-024 1 1
## LUNG1-025 1 1
## LUNG1-026 1 1
## LUNG1-029 1 1
## LUNG1-030 1 1
## LUNG1-031 1 1
## LUNG1-032 1 1
## LUNG1-033 1 1
## LUNG1-034 1 1
## LUNG1-035 1 1
## LUNG1-036 1 1
## LUNG1-037 1 1
## LUNG1-038 1 1
## LUNG1-040 1 1
## LUNG1-041 1 1
## LUNG1-042 1 1
## LUNG1-043 1 1
## LUNG1-045 1 1
## LUNG1-046 1 1
## LUNG1-047 1 1
## LUNG1-048 1 1
## LUNG1-049 1 1
## LUNG1-050 1 1
## LUNG1-051 1 1
## LUNG1-052 1 1
## LUNG1-053 1 1
## LUNG1-054 1 1
## LUNG1-055 1 1
## LUNG1-057 1 1
## LUNG1-058 1 1
## LUNG1-059 1 1
## LUNG1-060 1 1
## LUNG1-061 1 1
## LUNG1-062 1 1
## LUNG1-063 1 1
## LUNG1-064 1 1
## LUNG1-065 1 1
## LUNG1-067 1 1
## LUNG1-068 1 1
## LUNG1-070 1 1
## LUNG1-071 1 1
## LUNG1-072 1 1
## LUNG1-073 1 1
## LUNG1-074 1 1
## LUNG1-075 1 1
## LUNG1-076 1 1
## LUNG1-077 1 1
## LUNG1-078 1 1
## LUNG1-079 1 1
## LUNG1-081 1 1
## LUNG1-082 1 1
## LUNG1-084 1 1
## LUNG1-085 1 1
## LUNG1-086 1 1
## LUNG1-088 1 1
## LUNG1-089 1 1
## LUNG1-091 1 1
## LUNG1-092 1 1
## LUNG1-093 1 1
## LUNG1-095 1 1
## LUNG1-096 1 1
## LUNG1-097 1 1
## LUNG1-098 1 1
## LUNG1-099 1 1
## LUNG1-101 1 1
## LUNG1-102 1 1
## LUNG1-103 1 1
## LUNG1-104 1 1
## LUNG1-105 1 1
## LUNG1-106 1 1
## LUNG1-107 1 1
## LUNG1-108 1 1
## LUNG1-109 1 1
## LUNG1-110 1 1
## LUNG1-112 1 1
## LUNG1-113 1 1
## LUNG1-114 1 1
## LUNG1-115 1 1
## LUNG1-116 1 1
## LUNG1-117 1 1
## LUNG1-118 1 1
## LUNG1-119 1 1
## LUNG1-120 1 1
## LUNG1-121 1 1
## LUNG1-122 1 1
## LUNG1-123 1 1
## LUNG1-124 1 1
## LUNG1-125 1 1
## LUNG1-126 1 1
## LUNG1-127 1 1
## LUNG1-129 1 1
## LUNG1-130 1 1
## LUNG1-131 1 1
## LUNG1-132 1 1
## LUNG1-134 1 1
## LUNG1-135 1 1
## LUNG1-137 1 1
## LUNG1-139 1 1
## LUNG1-140 1 1
## LUNG1-141 1 1
## LUNG1-142 1 1
## LUNG1-143 1 1
## LUNG1-144 1 1
## LUNG1-145 1 1
## LUNG1-146 1 1
## LUNG1-147 1 1
## LUNG1-148 1 1
## LUNG1-149 1 1
## LUNG1-150 1 1
## LUNG1-151 1 1
## LUNG1-152 1 1
## LUNG1-153 1 1
## LUNG1-155 1 1
## LUNG1-156 1 1
## LUNG1-158 1 1
## LUNG1-159 1 1
## LUNG1-160 1 1
## LUNG1-162 1 1
## LUNG1-163 1 1
## LUNG1-164 1 1
## LUNG1-165 1 1
## LUNG1-166 1 1
## LUNG1-167 1 1
## LUNG1-168 1 1
## LUNG1-169 1 1
## LUNG1-170 1 1
## LUNG1-172 1 1
## LUNG1-173 1 1
## LUNG1-174 1 1
## LUNG1-175 1 1
## LUNG1-176 1 1
## LUNG1-177 1 1
## LUNG1-178 1 1
## LUNG1-179 1 1
## LUNG1-180 1 1
## LUNG1-181 1 1
## LUNG1-182 1 1
## LUNG1-183 1 1
## LUNG1-184 1 1
## LUNG1-186 1 1
## LUNG1-187 1 1
## LUNG1-188 1 1
## LUNG1-189 1 1
## LUNG1-190 1 1
## LUNG1-191 1 1
## LUNG1-192 1 1
## LUNG1-193 1 1
## LUNG1-195 1 1
## LUNG1-196 1 1
## LUNG1-197 1 1
## LUNG1-198 1 1
## LUNG1-199 1 1
## LUNG1-200 1 1
## LUNG1-201 1 1
## LUNG1-202 1 1
## LUNG1-203 1 1
## LUNG1-204 1 1
## LUNG1-205 1 1
## LUNG1-206 1 1
## LUNG1-207 1 1
## LUNG1-208 1 1
## LUNG1-209 1 1
## LUNG1-210 1 1
## LUNG1-211 1 1
## LUNG1-212 1 1
## LUNG1-213 1 1
## LUNG1-214 1 1
## LUNG1-215 1 1
## LUNG1-216 1 1
## LUNG1-217 1 1
## LUNG1-218 1 1
## LUNG1-219 1 1
## LUNG1-220 1 1
## LUNG1-221 1 1
## LUNG1-222 1 1
## LUNG1-223 1 1
## LUNG1-224 1 1
## LUNG1-225 1 1
## LUNG1-226 1 1
## LUNG1-227 1 1
## LUNG1-228 1 1
## LUNG1-229 1 1
## LUNG1-230 1 1
## LUNG1-231 1 1
## LUNG1-232 1 1
## LUNG1-233 1 1
## LUNG1-234 1 1
## LUNG1-235 1 1
## LUNG1-236 1 1
## LUNG1-237 1 1
## LUNG1-238 1 1
## LUNG1-239 1 1
## LUNG1-240 1 1
## LUNG1-242 1 1
## LUNG1-243 1 1
## LUNG1-244 1 1
## LUNG1-245 1 1
## LUNG1-247 1 1
## LUNG1-248 1 1
## LUNG1-250 1 1
## LUNG1-251 1 1
## LUNG1-252 1 1
## LUNG1-253 1 1
## LUNG1-254 1 1
## LUNG1-256 1 1
## LUNG1-257 1 1
## LUNG1-258 1 1
## LUNG1-259 1 1
## LUNG1-261 1 1
## LUNG1-262 1 1
## LUNG1-263 1 1
## LUNG1-264 1 1
## LUNG1-265 1 1
## LUNG1-266 1 1
## LUNG1-267 1 1
## LUNG1-268 1 1
## LUNG1-269 1 1
## LUNG1-270 1 1
## LUNG1-271 1 1
## LUNG1-272 1 1
## LUNG1-273 1 1
## LUNG1-274 1 1
## LUNG1-275 1 1
## LUNG1-276 1 1
## LUNG1-277 1 1
## LUNG1-279 1 1
## LUNG1-280 1 1
## LUNG1-281 1 1
## LUNG1-282 1 1
## LUNG1-283 1 1
## LUNG1-284 1 1
## LUNG1-285 1 1
## LUNG1-286 1 1
## LUNG1-288 1 1
## LUNG1-289 1 1
## LUNG1-290 1 1
## LUNG1-291 1 1
## LUNG1-292 1 1
## LUNG1-293 1 1
## LUNG1-294 1 1
## LUNG1-295 1 1
## LUNG1-296 1 1
## LUNG1-297 1 1
## LUNG1-298 1 1
## LUNG1-299 1 1
## LUNG1-300 1 1
## LUNG1-301 1 1
## LUNG1-302 1 1
## LUNG1-303 1 1
## LUNG1-304 1 1
## LUNG1-307 1 1
## LUNG1-308 1 1
## LUNG1-310 1 1
## LUNG1-311 1 1
## LUNG1-312 1 1
## LUNG1-313 1 1
## LUNG1-314 1 1
## LUNG1-316 1 1
## LUNG1-317 1 1
## LUNG1-318 1 1
## LUNG1-319 1 1
## LUNG1-320 1 1
## LUNG1-321 1 1
## LUNG1-322 1 1
## LUNG1-323 1 1
## LUNG1-324 1 1
## LUNG1-325 1 1
## LUNG1-326 1 1
## LUNG1-327 1 1
## LUNG1-329 1 1
## LUNG1-330 1 1
## LUNG1-331 1 1
## LUNG1-333 1 1
## LUNG1-334 1 1
## LUNG1-335 1 1
## LUNG1-336 1 1
## LUNG1-337 1 1
## LUNG1-339 1 1
## LUNG1-340 1 1
## LUNG1-341 1 1
## LUNG1-342 1 1
## LUNG1-343 1 1
## LUNG1-344 1 1
## LUNG1-345 1 1
## LUNG1-346 1 1
## LUNG1-347 1 1
## LUNG1-348 1 1
## LUNG1-349 1 1
## LUNG1-350 1 1
## LUNG1-351 1 1
## LUNG1-352 1 1
## LUNG1-354 1 1
## LUNG1-355 1 1
## LUNG1-356 1 1
## LUNG1-357 1 1
## LUNG1-358 1 1
## LUNG1-359 1 1
## LUNG1-360 1 1
## LUNG1-361 1 1
## LUNG1-362 1 1
## LUNG1-363 1 1
## LUNG1-364 1 1
## LUNG1-365 1 1
## LUNG1-366 1 1
## LUNG1-367 1 1
## LUNG1-368 1 1
## LUNG1-369 1 1
## LUNG1-370 1 1
## LUNG1-371 1 1
## LUNG1-372 1 1
## LUNG1-373 1 1
## LUNG1-374 1 1
## LUNG1-375 1 1
## LUNG1-376 1 1
## LUNG1-377 1 1
## LUNG1-378 1 1
## LUNG1-379 1 1
## LUNG1-380 1 1
## LUNG1-381 1 1
## LUNG1-382 1 1
## LUNG1-383 1 1
## LUNG1-384 1 1
## LUNG1-385 1 1
## LUNG1-386 1 1
## LUNG1-387 1 1
## LUNG1-388 1 1
## LUNG1-389 1 1
## LUNG1-390 1 1
## LUNG1-391 1 1
## LUNG1-392 1 1
## LUNG1-393 1 1
## LUNG1-395 1 1
## LUNG1-396 1 1
## LUNG1-397 1 1
## LUNG1-398 1 1
## LUNG1-399 1 1
## LUNG1-400 1 1
## LUNG1-401 1 1
## LUNG1-403 1 1
## LUNG1-404 1 1
## LUNG1-405 1 1
## LUNG1-406 1 1
## LUNG1-407 1 1
## LUNG1-408 1 1
## LUNG1-409 1 1
## LUNG1-410 1 1
## LUNG1-411 1 1
## LUNG1-412 1 1
## LUNG1-413 1 1
## LUNG1-414 1 1
## LUNG1-415 1 1
## LUNG1-416 1 1
## LUNG1-417 1 1
## LUNG1-418 1 1
## LUNG1-419 1 1
## LUNG1-420 1 1
## LUNG1-421 1 1
## log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis
## LUNG1-001 1
## LUNG1-002 1
## LUNG1-003 1
## LUNG1-004 1
## LUNG1-005 1
## LUNG1-006 1
## LUNG1-007 1
## LUNG1-008 1
## LUNG1-009 1
## LUNG1-010 1
## LUNG1-011 1
## LUNG1-012 1
## LUNG1-013 1
## LUNG1-014 1
## LUNG1-015 1
## LUNG1-016 1
## LUNG1-017 1
## LUNG1-018 1
## LUNG1-019 1
## LUNG1-022 1
## LUNG1-024 1
## LUNG1-025 1
## LUNG1-026 1
## LUNG1-029 1
## LUNG1-030 1
## LUNG1-031 1
## LUNG1-032 1
## LUNG1-033 1
## LUNG1-034 1
## LUNG1-035 1
## LUNG1-036 1
## LUNG1-037 1
## LUNG1-038 1
## LUNG1-040 1
## LUNG1-041 1
## LUNG1-042 1
## LUNG1-043 1
## LUNG1-045 1
## LUNG1-046 1
## LUNG1-047 1
## LUNG1-048 1
## LUNG1-049 1
## LUNG1-050 1
## LUNG1-051 1
## LUNG1-052 1
## LUNG1-053 1
## LUNG1-054 1
## LUNG1-055 1
## LUNG1-057 1
## LUNG1-058 1
## LUNG1-059 1
## LUNG1-060 1
## LUNG1-061 1
## LUNG1-062 1
## LUNG1-063 1
## LUNG1-064 1
## LUNG1-065 1
## LUNG1-067 1
## LUNG1-068 1
## LUNG1-070 1
## LUNG1-071 1
## LUNG1-072 1
## LUNG1-073 1
## LUNG1-074 1
## LUNG1-075 1
## LUNG1-076 1
## LUNG1-077 1
## LUNG1-078 1
## LUNG1-079 1
## LUNG1-081 1
## LUNG1-082 1
## LUNG1-084 1
## LUNG1-085 1
## LUNG1-086 1
## LUNG1-088 1
## LUNG1-089 1
## LUNG1-091 1
## LUNG1-092 1
## LUNG1-093 1
## LUNG1-095 1
## LUNG1-096 1
## LUNG1-097 1
## LUNG1-098 1
## LUNG1-099 1
## LUNG1-101 1
## LUNG1-102 1
## LUNG1-103 1
## LUNG1-104 1
## LUNG1-105 1
## LUNG1-106 1
## LUNG1-107 1
## LUNG1-108 1
## LUNG1-109 1
## LUNG1-110 1
## LUNG1-112 1
## LUNG1-113 1
## LUNG1-114 1
## LUNG1-115 1
## LUNG1-116 1
## LUNG1-117 1
## LUNG1-118 1
## LUNG1-119 1
## LUNG1-120 1
## LUNG1-121 1
## LUNG1-122 1
## LUNG1-123 1
## LUNG1-124 1
## LUNG1-125 1
## LUNG1-126 1
## LUNG1-127 1
## LUNG1-129 1
## LUNG1-130 1
## LUNG1-131 1
## LUNG1-132 1
## LUNG1-134 1
## LUNG1-135 1
## LUNG1-137 1
## LUNG1-139 1
## LUNG1-140 1
## LUNG1-141 1
## LUNG1-142 1
## LUNG1-143 1
## LUNG1-144 1
## LUNG1-145 1
## LUNG1-146 1
## LUNG1-147 1
## LUNG1-148 1
## LUNG1-149 1
## LUNG1-150 1
## LUNG1-151 1
## LUNG1-152 1
## LUNG1-153 1
## LUNG1-155 1
## LUNG1-156 1
## LUNG1-158 1
## LUNG1-159 1
## LUNG1-160 1
## LUNG1-162 1
## LUNG1-163 1
## LUNG1-164 1
## LUNG1-165 1
## LUNG1-166 1
## LUNG1-167 1
## LUNG1-168 1
## LUNG1-169 1
## LUNG1-170 1
## LUNG1-172 1
## LUNG1-173 1
## LUNG1-174 1
## LUNG1-175 1
## LUNG1-176 1
## LUNG1-177 1
## LUNG1-178 1
## LUNG1-179 1
## LUNG1-180 1
## LUNG1-181 1
## LUNG1-182 1
## LUNG1-183 1
## LUNG1-184 1
## LUNG1-186 1
## LUNG1-187 1
## LUNG1-188 1
## LUNG1-189 1
## LUNG1-190 1
## LUNG1-191 1
## LUNG1-192 1
## LUNG1-193 1
## LUNG1-195 1
## LUNG1-196 1
## LUNG1-197 1
## LUNG1-198 1
## LUNG1-199 1
## LUNG1-200 1
## LUNG1-201 1
## LUNG1-202 1
## LUNG1-203 1
## LUNG1-204 1
## LUNG1-205 1
## LUNG1-206 1
## LUNG1-207 1
## LUNG1-208 1
## LUNG1-209 1
## LUNG1-210 1
## LUNG1-211 1
## LUNG1-212 1
## LUNG1-213 1
## LUNG1-214 1
## LUNG1-215 1
## LUNG1-216 1
## LUNG1-217 1
## LUNG1-218 1
## LUNG1-219 1
## LUNG1-220 1
## LUNG1-221 1
## LUNG1-222 1
## LUNG1-223 1
## LUNG1-224 1
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## wavelet.LHH_glcm_Imc1 log.sigma.5.0.mm.3D_firstorder_Kurtosis
## LUNG1-001 1 1
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## LUNG1-350 1 1
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## LUNG1-379 1 1
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## LUNG1-400 1 1
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## LUNG1-418 1 1
## LUNG1-419 1 1
## LUNG1-420 1 1
## LUNG1-421 1 1
## log.sigma.5.0.mm.3D_firstorder_90Percentile
## LUNG1-001 1
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## LUNG1-004 1
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## wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis
## LUNG1-001 1
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## LUNG1-376 1
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## LUNG1-378 1
## LUNG1-379 1
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## LUNG1-381 1
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## LUNG1-417 1
## LUNG1-418 1
## LUNG1-419 1
## LUNG1-420 1
## LUNG1-421 1
## wavelet.HLH_glcm_DifferenceAverage
## LUNG1-001 1
## LUNG1-002 1
## LUNG1-003 1
## LUNG1-004 1
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## LUNG1-006 1
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## LUNG1-145 1
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## LUNG1-150 1
## LUNG1-151 1
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## LUNG1-153 1
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## LUNG1-159 1
## LUNG1-160 1
## LUNG1-162 1
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## LUNG1-165 1
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## LUNG1-167 1
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## LUNG1-170 1
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## LUNG1-180 1
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## LUNG1-183 1
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## LUNG1-186 1
## LUNG1-187 1
## LUNG1-188 1
## LUNG1-189 1
## LUNG1-190 1
## LUNG1-191 1
## LUNG1-192 1
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## LUNG1-198 1
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## LUNG1-200 1
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## LUNG1-205 1
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## LUNG1-210 1
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## LUNG1-212 1
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## LUNG1-215 1
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## LUNG1-219 1
## LUNG1-220 1
## LUNG1-221 1
## LUNG1-222 1
## LUNG1-223 1
## LUNG1-224 1
## LUNG1-225 1
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## LUNG1-228 1
## LUNG1-229 1
## LUNG1-230 1
## LUNG1-231 1
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## LUNG1-233 1
## LUNG1-234 1
## LUNG1-235 1
## LUNG1-236 1
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## LUNG1-240 1
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## LUNG1-250 1
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## LUNG1-253 1
## LUNG1-254 1
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## LUNG1-257 1
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## LUNG1-259 1
## LUNG1-261 1
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## LUNG1-263 1
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## LUNG1-265 1
## LUNG1-266 1
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## LUNG1-270 1
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## LUNG1-273 1
## LUNG1-274 1
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## LUNG1-280 1
## LUNG1-281 1
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## LUNG1-283 1
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## LUNG1-286 1
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## LUNG1-294 1
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## LUNG1-307 1
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## LUNG1-317 1
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## LUNG1-331 1
## LUNG1-333 1
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## LUNG1-335 1
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## LUNG1-337 1
## LUNG1-339 1
## LUNG1-340 1
## LUNG1-341 1
## LUNG1-342 1
## LUNG1-343 1
## LUNG1-344 1
## LUNG1-345 1
## LUNG1-346 1
## LUNG1-347 1
## LUNG1-348 1
## LUNG1-349 1
## LUNG1-350 1
## LUNG1-351 1
## LUNG1-352 1
## LUNG1-354 1
## LUNG1-355 1
## LUNG1-356 1
## LUNG1-357 1
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## LUNG1-359 1
## LUNG1-360 1
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## LUNG1-362 1
## LUNG1-363 1
## LUNG1-364 1
## LUNG1-365 1
## LUNG1-366 1
## LUNG1-367 1
## LUNG1-368 1
## LUNG1-369 1
## LUNG1-370 1
## LUNG1-371 1
## LUNG1-372 1
## LUNG1-373 1
## LUNG1-374 1
## LUNG1-375 1
## LUNG1-376 1
## LUNG1-377 1
## LUNG1-378 1
## LUNG1-379 1
## LUNG1-380 1
## LUNG1-381 1
## LUNG1-382 1
## LUNG1-383 1
## LUNG1-384 1
## LUNG1-385 1
## LUNG1-386 1
## LUNG1-387 1
## LUNG1-388 1
## LUNG1-389 1
## LUNG1-390 1
## LUNG1-391 1
## LUNG1-392 1
## LUNG1-393 1
## LUNG1-395 1
## LUNG1-396 1
## LUNG1-397 1
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## LUNG1-399 1
## LUNG1-400 1
## LUNG1-401 1
## LUNG1-403 1
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## LUNG1-410 1
## LUNG1-411 1
## LUNG1-412 1
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## LUNG1-414 1
## LUNG1-415 1
## LUNG1-416 1
## LUNG1-417 1
## LUNG1-418 1
## LUNG1-419 1
## LUNG1-420 1
## LUNG1-421 1
## wavelet.HHH_firstorder_RootMeanSquared
## LUNG1-001 1
## LUNG1-002 1
## LUNG1-003 1
## LUNG1-004 1
## LUNG1-005 1
## LUNG1-006 1
## LUNG1-007 1
## LUNG1-008 1
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## LUNG1-010 1
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## LUNG1-012 1
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## LUNG1-015 1
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## LUNG1-017 1
## LUNG1-018 1
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## LUNG1-035 1
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## LUNG1-037 1
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## LUNG1-040 1
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## LUNG1-043 1
## LUNG1-045 1
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## LUNG1-048 1
## LUNG1-049 1
## LUNG1-050 1
## LUNG1-051 1
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## LUNG1-053 1
## LUNG1-054 1
## LUNG1-055 1
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## LUNG1-059 1
## LUNG1-060 1
## LUNG1-061 1
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## LUNG1-065 1
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## LUNG1-071 1
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## LUNG1-074 1
## LUNG1-075 1
## LUNG1-076 1
## LUNG1-077 1
## LUNG1-078 1
## LUNG1-079 1
## LUNG1-081 1
## LUNG1-082 1
## LUNG1-084 1
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## LUNG1-086 1
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## LUNG1-089 1
## LUNG1-091 1
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## LUNG1-095 1
## LUNG1-096 1
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## LUNG1-099 1
## LUNG1-101 1
## LUNG1-102 1
## LUNG1-103 1
## LUNG1-104 1
## LUNG1-105 1
## LUNG1-106 1
## LUNG1-107 1
## LUNG1-108 1
## LUNG1-109 1
## LUNG1-110 1
## LUNG1-112 1
## LUNG1-113 1
## LUNG1-114 1
## LUNG1-115 1
## LUNG1-116 1
## LUNG1-117 1
## LUNG1-118 1
## LUNG1-119 1
## LUNG1-120 1
## LUNG1-121 1
## LUNG1-122 1
## LUNG1-123 1
## LUNG1-124 1
## LUNG1-125 1
## LUNG1-126 1
## LUNG1-127 1
## LUNG1-129 1
## LUNG1-130 1
## LUNG1-131 1
## LUNG1-132 1
## LUNG1-134 1
## LUNG1-135 1
## LUNG1-137 1
## LUNG1-139 1
## LUNG1-140 1
## LUNG1-141 1
## LUNG1-142 1
## LUNG1-143 1
## LUNG1-144 1
## LUNG1-145 1
## LUNG1-146 1
## LUNG1-147 1
## LUNG1-148 1
## LUNG1-149 1
## LUNG1-150 1
## LUNG1-151 1
## LUNG1-152 1
## LUNG1-153 1
## LUNG1-155 1
## LUNG1-156 1
## LUNG1-158 1
## LUNG1-159 1
## LUNG1-160 1
## LUNG1-162 1
## LUNG1-163 1
## LUNG1-164 1
## LUNG1-165 1
## LUNG1-166 1
## LUNG1-167 1
## LUNG1-168 1
## LUNG1-169 1
## LUNG1-170 1
## LUNG1-172 1
## LUNG1-173 1
## LUNG1-174 1
## LUNG1-175 1
## LUNG1-176 1
## LUNG1-177 1
## LUNG1-178 1
## LUNG1-179 1
## LUNG1-180 1
## LUNG1-181 1
## LUNG1-182 1
## LUNG1-183 1
## LUNG1-184 1
## LUNG1-186 1
## LUNG1-187 1
## LUNG1-188 1
## LUNG1-189 1
## LUNG1-190 1
## LUNG1-191 1
## LUNG1-192 1
## LUNG1-193 1
## LUNG1-195 1
## LUNG1-196 1
## LUNG1-197 1
## LUNG1-198 1
## LUNG1-199 1
## LUNG1-200 1
## LUNG1-201 1
## LUNG1-202 1
## LUNG1-203 1
## LUNG1-204 1
## LUNG1-205 1
## LUNG1-206 1
## LUNG1-207 1
## LUNG1-208 1
## LUNG1-209 1
## LUNG1-210 1
## LUNG1-211 1
## LUNG1-212 1
## LUNG1-213 1
## LUNG1-214 1
## LUNG1-215 1
## LUNG1-216 1
## LUNG1-217 1
## LUNG1-218 1
## LUNG1-219 1
## LUNG1-220 1
## LUNG1-221 1
## LUNG1-222 1
## LUNG1-223 1
## LUNG1-224 1
## LUNG1-225 1
## LUNG1-226 1
## LUNG1-227 1
## LUNG1-228 1
## LUNG1-229 1
## LUNG1-230 1
## LUNG1-231 1
## LUNG1-232 1
## LUNG1-233 1
## LUNG1-234 1
## LUNG1-235 1
## LUNG1-236 1
## LUNG1-237 1
## LUNG1-238 1
## LUNG1-239 1
## LUNG1-240 1
## LUNG1-242 1
## LUNG1-243 1
## LUNG1-244 1
## LUNG1-245 1
## LUNG1-247 1
## LUNG1-248 1
## LUNG1-250 1
## LUNG1-251 1
## LUNG1-252 1
## LUNG1-253 1
## LUNG1-254 1
## LUNG1-256 1
## LUNG1-257 1
## LUNG1-258 1
## LUNG1-259 1
## LUNG1-261 1
## LUNG1-262 1
## LUNG1-263 1
## LUNG1-264 1
## LUNG1-265 1
## LUNG1-266 1
## LUNG1-267 1
## LUNG1-268 1
## LUNG1-269 1
## LUNG1-270 1
## LUNG1-271 1
## LUNG1-272 1
## LUNG1-273 1
## LUNG1-274 1
## LUNG1-275 1
## LUNG1-276 1
## LUNG1-277 1
## LUNG1-279 1
## LUNG1-280 1
## LUNG1-281 1
## LUNG1-282 1
## LUNG1-283 1
## LUNG1-284 1
## LUNG1-285 1
## LUNG1-286 1
## LUNG1-288 1
## LUNG1-289 1
## LUNG1-290 1
## LUNG1-291 1
## LUNG1-292 1
## LUNG1-293 1
## LUNG1-294 1
## LUNG1-295 1
## LUNG1-296 1
## LUNG1-297 1
## LUNG1-298 1
## LUNG1-299 1
## LUNG1-300 1
## LUNG1-301 1
## LUNG1-302 1
## LUNG1-303 1
## LUNG1-304 1
## LUNG1-307 1
## LUNG1-308 1
## LUNG1-310 1
## LUNG1-311 1
## LUNG1-312 1
## LUNG1-313 1
## LUNG1-314 1
## LUNG1-316 1
## LUNG1-317 1
## LUNG1-318 1
## LUNG1-319 1
## LUNG1-320 1
## LUNG1-321 1
## LUNG1-322 1
## LUNG1-323 1
## LUNG1-324 1
## LUNG1-325 1
## LUNG1-326 1
## LUNG1-327 1
## LUNG1-329 1
## LUNG1-330 1
## LUNG1-331 1
## LUNG1-333 1
## LUNG1-334 1
## LUNG1-335 1
## LUNG1-336 1
## LUNG1-337 1
## LUNG1-339 1
## LUNG1-340 1
## LUNG1-341 1
## LUNG1-342 1
## LUNG1-343 1
## LUNG1-344 1
## LUNG1-345 1
## LUNG1-346 1
## LUNG1-347 1
## LUNG1-348 1
## LUNG1-349 1
## LUNG1-350 1
## LUNG1-351 1
## LUNG1-352 1
## LUNG1-354 1
## LUNG1-355 1
## LUNG1-356 1
## LUNG1-357 1
## LUNG1-358 1
## LUNG1-359 1
## LUNG1-360 1
## LUNG1-361 1
## LUNG1-362 1
## LUNG1-363 1
## LUNG1-364 1
## LUNG1-365 1
## LUNG1-366 1
## LUNG1-367 1
## LUNG1-368 1
## LUNG1-369 1
## LUNG1-370 1
## LUNG1-371 1
## LUNG1-372 1
## LUNG1-373 1
## LUNG1-374 1
## LUNG1-375 1
## LUNG1-376 1
## LUNG1-377 1
## LUNG1-378 1
## LUNG1-379 1
## LUNG1-380 1
## LUNG1-381 1
## LUNG1-382 1
## LUNG1-383 1
## LUNG1-384 1
## LUNG1-385 1
## LUNG1-386 1
## LUNG1-387 1
## LUNG1-388 1
## LUNG1-389 1
## LUNG1-390 1
## LUNG1-391 1
## LUNG1-392 1
## LUNG1-393 1
## LUNG1-395 1
## LUNG1-396 1
## LUNG1-397 1
## LUNG1-398 1
## LUNG1-399 1
## LUNG1-400 1
## LUNG1-401 1
## LUNG1-403 1
## LUNG1-404 1
## LUNG1-405 1
## LUNG1-406 1
## LUNG1-407 1
## LUNG1-408 1
## LUNG1-409 1
## LUNG1-410 1
## LUNG1-411 1
## LUNG1-412 1
## LUNG1-413 1
## LUNG1-414 1
## LUNG1-415 1
## LUNG1-416 1
## LUNG1-417 1
## LUNG1-418 1
## LUNG1-419 1
## LUNG1-420 1
## LUNG1-421 1
## wavelet.LHL_gldm_DependenceVariance
## LUNG1-001 1
## LUNG1-002 1
## LUNG1-003 1
## LUNG1-004 1
## LUNG1-005 1
## LUNG1-006 1
## LUNG1-007 1
## LUNG1-008 1
## LUNG1-009 1
## LUNG1-010 1
## LUNG1-011 1
## LUNG1-012 1
## LUNG1-013 1
## LUNG1-014 1
## LUNG1-015 1
## LUNG1-016 1
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##
## Slot "priors":
## alpha beta lambda delta
## [1,] 1 1 0.59294733 0.01
## [2,] 1 1 0.58293556 0.01
## [3,] 1 1 0.09898599 0.01
## [4,] 1 1 0.23875294 0.01
## [5,] 1 1 0.20297480 0.01
## [6,] 1 1 0.05089137 0.01
## [7,] 1 1 0.54851252 0.01
## [8,] 1 1 0.04751908 0.01
## [9,] 1 1 0.05618775 0.01
## [10,] 1 1 0.31534701 0.01
## [11,] 1 1 0.06305280 0.01
## [12,] 1 1 0.07558030 0.01
## [13,] 1 1 0.27407521 0.01
## [14,] 1 1 0.58659284 0.01
## [15,] 1 1 0.58627416 0.01
## [16,] 1 1 0.09983517 0.01
## [17,] 1 1 0.16303270 0.01
## [18,] 1 1 0.06736884 0.01
## [19,] 1 1 0.43709911 0.01
## [20,] 1 1 0.72841699 0.01
## [21,] 1 1 0.60188137 0.01
## [22,] 1 1 0.32335874 0.01
## [23,] 1 1 0.18801457 0.01
## [24,] 1 1 0.47310009 0.01
## [25,] 1 1 0.43354508 0.01
## [26,] 1 1 0.68236814 0.01
## [27,] 1 1 0.10506506 0.01
## [28,] 1 1 0.84772229 0.01
## [29,] 1 1 0.21294393 0.01
## [30,] 1 1 0.25662298 0.01
## [31,] 1 1 0.78158789 0.01
## [32,] 1 1 0.35382248 0.01
##
##
## Slot "dataInteger":
## An object of class "VSLCMdataInteger"
## Slot "n":
## numeric(0)
##
## Slot "d":
## numeric(0)
##
## Slot "data":
## [,1]
## [1,] NA
##
## Slot "notNA":
## [,1]
## [1,] NA
##
## Slot "priors":
## [,1]
## [1,] NA
##
##
## Slot "dataCategorical":
## An object of class "VSLCMdataCategorical"
## Slot "n":
## [1] 377
##
## Slot "d":
## [1] 1
##
## Slot "data":
## gender
## [1,] "male"
## [2,] "male"
## [3,] "male"
## [4,] "male"
## [5,] "male"
## [6,] "male"
## [7,] "male"
## [8,] "male"
## [9,] "male"
## [10,] "female"
## [11,] "male"
## [12,] "male"
## [13,] "male"
## [14,] "male"
## [15,] "male"
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## [17,] "male"
## [18,] "male"
## [19,] "male"
## [20,] "male"
## [21,] "female"
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## [24,] "female"
## [25,] "male"
## [26,] "male"
## [27,] "male"
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## [31,] "female"
## [32,] "male"
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## [34,] "female"
## [35,] "female"
## [36,] "male"
## [37,] "male"
## [38,] "female"
## [39,] "male"
## [40,] "male"
## [41,] "female"
## [42,] "male"
## [43,] "male"
## [44,] "male"
## [45,] "male"
## [46,] "male"
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## [48,] "male"
## [49,] "male"
## [50,] "male"
## [51,] "male"
## [52,] "male"
## [53,] "male"
## [54,] "female"
## [55,] "female"
## [56,] "male"
## [57,] "male"
## [58,] "male"
## [59,] "male"
## [60,] "male"
## [61,] "female"
## [62,] "male"
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## [64,] "male"
## [65,] "female"
## [66,] "male"
## [67,] "female"
## [68,] "male"
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## [70,] "male"
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## [91,] "male"
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## [94,] "female"
## [95,] "male"
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## [98,] "male"
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## [100,] "male"
## [101,] "male"
## [102,] "female"
## [103,] "male"
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## [114,] "male"
## [115,] "male"
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## [120,] "male"
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## [123,] "male"
## [124,] "male"
## [125,] "male"
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## [127,] "male"
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## [198,] "male"
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## [202,] "male"
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## [204,] "male"
## [205,] "male"
## [206,] "male"
## [207,] "male"
## [208,] "female"
## [209,] "female"
## [210,] "male"
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## [212,] "male"
## [213,] "male"
## [214,] "male"
## [215,] "male"
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## [218,] "female"
## [219,] "female"
## [220,] "male"
## [221,] "female"
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## [225,] "male"
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## [229,] "female"
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## [279,] "male"
## [280,] "male"
## [281,] "female"
## [282,] "male"
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## [284,] "male"
## [285,] "male"
## [286,] "male"
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## [288,] "male"
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## [291,] "male"
## [292,] "male"
## [293,] "male"
## [294,] "female"
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## [374,] "male"
## [375,] "male"
## [376,] "male"
## [377,] "female"
##
## Slot "shortdata":
## gender
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## [327,] 1
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## [331,] 1
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## [351,] 2
## [352,] 1
## [353,] 1
## [354,] 1
## [355,] 2
## [356,] 1
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## [358,] 2
## [359,] 1
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## [363,] 1
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## [367,] 1
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## [369,] 1
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## [371,] 1
## [372,] 2
## [373,] 2
## [374,] 2
## [375,] 2
## [376,] 2
## [377,] 1
##
## Slot "weightdata":
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [371] 1 1 1 1 1 1 1
##
## Slot "modalitynames":
## [[1]]
## [1] "female" "male"
##
##
##
## Slot "var.names":
## [1] "gender"
## [2] "age"
## [3] "original_shape_VoxelVolume"
## [4] "wavelet.HHL_glcm_ClusterProminence"
## [5] "log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis"
## [6] "log.sigma.2.0.mm.3D_glcm_Autocorrelation"
## [7] "log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis"
## [8] "wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis"
## [9] "wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis"
## [10] "wavelet.HHH_glszm_GrayLevelNonUniformity"
## [11] "wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis"
## [12] "wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis"
## [13] "wavelet.HHH_glcm_ClusterProminence"
## [14] "wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis"
## [15] "wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis"
## [16] "wavelet.LLL_glcm_ClusterProminence"
## [17] "wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis"
## [18] "wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis"
## [19] "wavelet.HHH_glrlm_GrayLevelVariance"
## [20] "log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized"
## [21] "wavelet.HHH_glcm_Imc1"
## [22] "original_glcm_Imc1"
## [23] "log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis"
## [24] "wavelet.LHH_glszm_GrayLevelNonUniformity"
## [25] "wavelet.LHH_firstorder_Skewness"
## [26] "wavelet.HHH_glszm_GrayLevelNonUniformityNormalized"
## [27] "wavelet.LHH_glcm_Imc1"
## [28] "log.sigma.5.0.mm.3D_firstorder_Kurtosis"
## [29] "log.sigma.5.0.mm.3D_firstorder_90Percentile"
## [30] "wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis"
## [31] "wavelet.HLH_glcm_DifferenceAverage"
## [32] "wavelet.HHH_firstorder_RootMeanSquared"
## [33] "wavelet.LHL_gldm_DependenceVariance"
##
##
## Slot "criteria":
## An object of class "VSLCMcriteria"
## Slot "loglikelihood":
## [1] 14936.65
##
## Slot "AIC":
## gender
## 14358.65
##
## Slot "BIC":
## [1] 13222.23
##
## Slot "ICL":
## [1] 1497.006
##
## Slot "MICL":
## numeric(0)
##
## Slot "nbparam":
## [1] 578
##
## Slot "cvrate":
## numeric(0)
##
## Slot "degeneracyrate":
## [1] 0
##
## Slot "discrim":
## original_shape_VoxelVolume
## 49.744663
## wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis
## 44.807389
## wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis
## 20.138258
## wavelet.HHH_glszm_GrayLevelNonUniformityNormalized
## -8.383695
## log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis
## -9.247997
## gender
## -10.408503
## log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis
## -17.078872
## wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis
## -27.426101
## wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis
## -56.505047
## wavelet.LHH_glszm_GrayLevelNonUniformity
## -56.885904
## wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis
## -66.929075
## wavelet.LHL_gldm_DependenceVariance
## -68.084664
## wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis
## -71.110547
## wavelet.HHH_glcm_Imc1
## -110.516422
## wavelet.LLL_glcm_ClusterProminence
## -112.506137
## wavelet.HLH_glcm_DifferenceAverage
## -119.380174
## wavelet.LHH_glcm_Imc1
## -122.157965
## log.sigma.2.0.mm.3D_glcm_Autocorrelation
## -125.787928
## age
## -131.567055
## wavelet.HHL_glcm_ClusterProminence
## -132.839662
## log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized
## -141.798654
## wavelet.HHH_glrlm_GrayLevelVariance
## -157.819072
## wavelet.HHH_glszm_GrayLevelNonUniformity
## -166.395103
## original_glcm_Imc1
## -182.336688
## wavelet.HHH_firstorder_RootMeanSquared
## -184.473330
## wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis
## -190.917260
## wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis
## -194.076794
## log.sigma.5.0.mm.3D_firstorder_Kurtosis
## -198.539905
## log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis
## -225.805363
## wavelet.LHH_firstorder_Skewness
## -244.469507
## log.sigma.5.0.mm.3D_firstorder_90Percentile
## -273.175828
## wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis
## -276.669571
## wavelet.HHH_glcm_ClusterProminence
## -279.311957
##
##
## Slot "partitions":
## An object of class "VSLCMpartitions"
## Slot "zMAP":
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##
## Slot "zOPT":
## numeric(0)
##
## Slot "tik":
## class-1 class-2 class-3 class-4 class-5
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## [10,] 7.103711e-150 0.000000e+00 1.927706e-08 2.073515e-06 1.688494e-39
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## [52,] 0.000000e+00 1.198922e-141 3.578886e-04 6.879948e-56 3.620391e-26
## [53,] 1.000000e+00 8.764300e-143 2.320002e-86 1.662951e-09 0.000000e+00
## [54,] 0.000000e+00 0.000000e+00 7.204387e-85 3.794958e-12 0.000000e+00
## [55,] 3.985938e-34 1.368738e-39 2.485454e-09 1.000000e+00 3.430092e-245
## [56,] 7.163342e-53 5.443412e-168 5.094856e-06 9.999949e-01 5.903707e-161
## [57,] 0.000000e+00 1.000000e+00 2.499910e-15 9.875906e-80 2.023892e-31
## [58,] 1.170887e-105 7.619174e-17 2.644556e-15 1.588345e-13 3.593804e-283
## [59,] 5.378346e-188 7.375449e-155 9.999998e-01 3.140729e-16 4.743275e-57
## [60,] 3.588114e-49 2.508509e-137 9.999640e-01 3.594756e-05 1.155595e-100
## [61,] 1.767830e-90 3.064026e-79 1.000000e+00 4.003994e-09 5.245985e-164
## [62,] 6.768699e-322 0.000000e+00 5.806099e-19 5.820121e-40 2.826830e-22
## [63,] 3.360669e-152 9.962080e-01 1.677407e-04 3.872239e-22 3.218273e-50
## [64,] 1.374835e-162 6.748004e-03 9.932124e-01 5.435598e-23 2.311648e-30
## [65,] 5.762496e-88 1.000000e+00 1.057045e-09 1.658453e-30 1.551408e-28
## [66,] 3.023612e-76 0.000000e+00 1.496223e-23 9.999953e-01 7.730543e-202
## [67,] 2.918794e-154 0.000000e+00 1.000000e+00 9.881264e-28 1.791607e-53
## [68,] 1.153100e-07 3.913479e-117 9.213139e-80 2.716801e-11 0.000000e+00
## [69,] 5.180649e-172 0.000000e+00 1.000000e+00 7.392613e-23 4.355740e-80
## [70,] 8.176378e-09 7.515985e-141 1.908940e-78 5.640699e-03 0.000000e+00
## [71,] 3.373035e-11 1.257375e-77 1.562566e-47 6.545534e-10 0.000000e+00
## [72,] 0.000000e+00 0.000000e+00 0.000000e+00 2.521052e-72 1.665587e-247
## [73,] 1.218140e-23 6.993783e-19 5.101074e-15 2.911342e-08 1.530390e-167
## [74,] 1.000000e+00 0.000000e+00 0.000000e+00 1.342952e-67 0.000000e+00
## [75,] 0.000000e+00 0.000000e+00 4.751417e-61 2.771616e-09 3.117614e-57
## [76,] 0.000000e+00 0.000000e+00 2.058960e-281 1.222948e-42 0.000000e+00
## [77,] 0.000000e+00 1.000000e+00 2.231374e-50 0.000000e+00 2.584187e-24
## [78,] 7.130832e-127 9.968425e-01 3.157515e-03 2.710824e-26 3.955874e-54
## [79,] 1.394039e-129 9.996192e-01 3.807896e-04 2.193365e-25 8.379792e-43
## [80,] 0.000000e+00 0.000000e+00 7.333771e-45 1.631085e-163 1.000000e+00
## [81,] 0.000000e+00 0.000000e+00 1.976461e-17 2.181382e-68 3.747084e-19
## [82,] 0.000000e+00 1.970853e-270 1.200090e-15 8.836056e-115 4.964413e-20
## [83,] 1.000000e+00 0.000000e+00 2.687393e-242 4.505033e-33 0.000000e+00
## [84,] 3.998719e-157 0.000000e+00 9.933798e-01 3.160801e-21 1.351103e-34
## [85,] 1.521058e-100 2.353738e-11 9.999982e-01 3.472963e-18 4.429458e-45
## [86,] 3.784625e-09 7.310087e-67 1.104597e-25 1.000000e+00 0.000000e+00
## [87,] 3.359045e-03 1.720666e-94 1.028241e-52 5.784955e-04 0.000000e+00
## [88,] 9.244894e-137 9.630650e-136 9.628477e-01 1.781505e-16 5.894826e-23
## [89,] 0.000000e+00 0.000000e+00 3.894693e-269 0.000000e+00 1.000000e+00
## [90,] 9.999620e-01 1.144560e-103 3.829852e-62 2.678618e-05 0.000000e+00
## [91,] 7.343314e-104 0.000000e+00 1.000000e+00 1.255488e-08 4.551102e-35
## [92,] 1.000000e+00 0.000000e+00 1.667881e-158 7.139528e-28 0.000000e+00
## [93,] 2.785355e-244 0.000000e+00 2.843200e-08 1.105367e-38 7.070495e-17
## [94,] 9.999999e-01 0.000000e+00 0.000000e+00 5.844845e-36 0.000000e+00
## [95,] 6.370218e-88 1.305733e-123 1.746921e-90 9.354227e-22 1.091416e-241
## [96,] 1.126184e-102 4.138137e-41 9.999999e-01 8.262570e-11 5.441069e-42
## [97,] 1.000000e+00 2.163021e-196 1.678628e-109 2.114200e-17 0.000000e+00
## [98,] 0.000000e+00 0.000000e+00 1.028249e-69 5.386904e-202 5.392735e-11
## [99,] 0.000000e+00 0.000000e+00 3.470344e-27 1.199445e-105 1.956156e-10
## [100,] 6.565633e-100 2.804353e-36 1.803573e-25 5.426255e-25 0.000000e+00
## [101,] 3.495676e-05 6.531455e-82 1.774062e-61 2.486402e-10 0.000000e+00
## [102,] 7.169936e-21 3.421899e-320 5.132580e-85 6.538627e-10 0.000000e+00
## [103,] 6.950155e-227 0.000000e+00 9.998908e-01 3.876218e-46 3.181736e-31
## [104,] 7.023825e-89 0.000000e+00 9.999685e-01 9.352464e-11 3.087156e-27
## [105,] 2.864189e-14 6.256336e-78 5.258397e-41 9.999918e-01 0.000000e+00
## [106,] 0.000000e+00 0.000000e+00 2.013880e-12 1.302109e-31 1.925973e-32
## [107,] 0.000000e+00 0.000000e+00 6.212407e-278 4.647115e-115 3.287893e-48
## [108,] 9.751178e-183 0.000000e+00 7.063260e-04 8.483895e-10 2.625680e-38
## [109,] 4.839929e-02 2.115438e-115 2.272249e-47 1.622284e-03 0.000000e+00
## [110,] 1.906702e-82 0.000000e+00 3.489152e-21 1.000000e+00 1.000249e-110
## [111,] 2.292675e-98 4.512337e-15 1.276050e-06 6.013645e-13 2.935481e-80
## [112,] 2.808011e-173 0.000000e+00 7.601757e-06 1.165693e-40 2.551472e-09
## [113,] 7.467639e-63 0.000000e+00 8.154123e-15 1.000000e+00 1.556157e-309
## [114,] 1.501261e-94 4.319670e-98 1.000000e+00 2.687755e-18 1.554155e-44
## [115,] 1.000000e+00 5.182622e-198 3.780190e-104 6.500499e-15 0.000000e+00
## [116,] 6.397848e-12 4.620999e-50 1.078117e-46 1.587612e-11 0.000000e+00
## [117,] 0.000000e+00 0.000000e+00 7.014538e-17 2.626116e-81 1.543091e-03
## [118,] 8.720948e-06 9.499678e-153 6.769675e-52 9.925867e-01 0.000000e+00
## [119,] 0.000000e+00 2.846525e-310 1.194119e-14 1.686804e-68 7.373273e-11
## [120,] 0.000000e+00 0.000000e+00 3.607437e-17 3.096673e-79 2.835114e-12
## [121,] 0.000000e+00 2.645531e-161 9.870646e-01 1.828674e-61 3.587034e-42
## [122,] 2.843426e-89 3.183665e-28 2.474754e-21 1.760679e-23 0.000000e+00
## [123,] 2.149947e-244 1.334391e-16 2.369445e-17 1.178548e-33 9.079694e-149
## [124,] 6.005738e-75 1.401568e-26 7.602070e-01 1.025517e-06 1.580780e-79
## [125,] 3.365967e-06 3.789194e-55 3.154367e-31 2.124245e-03 0.000000e+00
## [126,] 3.412455e-96 0.000000e+00 9.999999e-01 2.946981e-13 1.208620e-50
## [127,] 0.000000e+00 0.000000e+00 9.364514e-39 2.415875e-105 1.377667e-16
## [128,] 0.000000e+00 1.000000e+00 5.558095e-17 1.261176e-70 4.122462e-51
## [129,] 2.257657e-05 1.016473e-50 1.312701e-34 3.676402e-08 1.043053e-265
## [130,] 8.645201e-301 0.000000e+00 1.686503e-09 4.199329e-47 9.554047e-23
## [131,] 4.564109e-84 0.000000e+00 1.050519e-63 4.001959e-04 0.000000e+00
## [132,] 2.614322e-23 0.000000e+00 2.493215e-50 9.885655e-01 0.000000e+00
## [133,] 9.039637e-177 0.000000e+00 1.093568e-02 2.960283e-29 2.833052e-18
## [134,] 1.000000e+00 0.000000e+00 0.000000e+00 2.575446e-104 0.000000e+00
## [135,] 1.000000e+00 0.000000e+00 0.000000e+00 7.424853e-127 0.000000e+00
## [136,] 0.000000e+00 0.000000e+00 8.023577e-35 5.956353e-73 1.000000e+00
## [137,] 0.000000e+00 0.000000e+00 9.608732e-200 6.524708e-29 0.000000e+00
## [138,] 1.530687e-51 0.000000e+00 8.937527e-09 1.000000e+00 3.224796e-113
## [139,] 0.000000e+00 0.000000e+00 5.941211e-15 1.616737e-109 8.296226e-15
## [140,] 6.836889e-79 2.636117e-20 1.720625e-10 3.068060e-06 1.023788e-168
## [141,] 0.000000e+00 0.000000e+00 4.539835e-64 9.970009e-01 3.458460e-323
## [142,] 5.190565e-51 0.000000e+00 9.999905e-01 9.493741e-06 3.704632e-92
## [143,] 0.000000e+00 0.000000e+00 0.000000e+00 5.040535e-87 0.000000e+00
## [144,] 4.694722e-151 9.965772e-01 3.203351e-03 5.008100e-37 7.207591e-54
## [145,] 3.285058e-60 0.000000e+00 5.357012e-16 1.000000e+00 2.274366e-125
## [146,] 7.302342e-80 5.737976e-30 1.242094e-07 9.563281e-06 2.205077e-272
## [147,] 0.000000e+00 0.000000e+00 4.797818e-18 3.859146e-73 1.959735e-13
## [148,] 0.000000e+00 0.000000e+00 1.239759e-126 7.897617e-301 9.999995e-01
## [149,] 1.894537e-10 9.156298e-44 5.862804e-32 1.445675e-06 6.590830e-192
## [150,] 4.207579e-106 2.862371e-14 1.471726e-02 1.241851e-07 7.104696e-120
## [151,] 5.833808e-04 3.580230e-78 1.791221e-36 9.993878e-01 0.000000e+00
## [152,] 0.000000e+00 0.000000e+00 9.015006e-33 2.425930e-41 5.460048e-19
## [153,] 0.000000e+00 0.000000e+00 7.793787e-114 6.515541e-17 0.000000e+00
## [154,] 0.000000e+00 0.000000e+00 7.331274e-30 3.411522e-117 1.881951e-07
## [155,] 1.659883e-87 0.000000e+00 6.686026e-17 1.000000e+00 1.499312e-192
## [156,] 0.000000e+00 0.000000e+00 1.423970e-56 1.829526e-153 1.000000e+00
## [157,] 3.287200e-19 7.869006e-70 3.493129e-44 1.668786e-07 0.000000e+00
## [158,] 9.056404e-12 3.480197e-122 1.018002e-58 1.000000e+00 1.158498e-212
## [159,] 5.271051e-23 6.747416e-89 4.047117e-12 1.000000e+00 0.000000e+00
## [160,] 0.000000e+00 0.000000e+00 9.994918e-01 1.506295e-44 6.441609e-48
## [161,] 4.512221e-75 0.000000e+00 2.279132e-03 9.977209e-01 6.837193e-140
## [162,] 1.000000e+00 1.192308e-263 3.736797e-159 3.870430e-32 0.000000e+00
## [163,] 5.696061e-312 0.000000e+00 2.506509e-13 9.621130e-29 4.537613e-39
## [164,] 0.000000e+00 2.469016e-220 1.097951e-05 1.675484e-46 4.220808e-18
## [165,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 2.514621e-18
## [166,] 1.000000e+00 0.000000e+00 0.000000e+00 5.540968e-68 0.000000e+00
## [167,] 0.000000e+00 0.000000e+00 9.996218e-01 1.510533e-33 7.278426e-58
## [168,] 6.704187e-29 0.000000e+00 3.183600e-136 2.932050e-23 0.000000e+00
## [169,] 0.000000e+00 0.000000e+00 9.751225e-88 2.357017e-315 9.572785e-37
## [170,] 0.000000e+00 0.000000e+00 7.455178e-68 2.654050e-63 3.405797e-35
## [171,] 8.587981e-152 0.000000e+00 9.955002e-01 3.177017e-06 3.823717e-80
## [172,] 7.339917e-268 0.000000e+00 1.939924e-05 1.656571e-30 4.514942e-18
## [173,] 1.080499e-245 0.000000e+00 2.925015e-04 6.025474e-42 4.233740e-16
## [174,] 4.173912e-306 1.553883e-117 9.936219e-01 3.002818e-32 4.395277e-31
## [175,] 5.737522e-08 1.258136e-58 1.225452e-39 1.365653e-08 0.000000e+00
## [176,] 1.059792e-71 0.000000e+00 9.993912e-01 6.088162e-04 6.103697e-128
## [177,] 0.000000e+00 0.000000e+00 1.786430e-38 9.999204e-01 2.020621e-109
## [178,] 0.000000e+00 0.000000e+00 1.114110e-31 4.543956e-50 1.072792e-25
## [179,] 0.000000e+00 0.000000e+00 2.499482e-155 3.491135e-18 0.000000e+00
## [180,] 2.365965e-63 4.543660e-07 9.996183e-01 1.672765e-14 1.792587e-84
## [181,] 4.299011e-190 0.000000e+00 8.036561e-10 4.707917e-13 3.124101e-45
## [182,] 3.731476e-98 4.732134e-18 1.716737e-04 2.911170e-08 8.949725e-123
## [183,] 0.000000e+00 0.000000e+00 2.239594e-41 1.452431e-93 1.000000e+00
## [184,] 1.080324e-244 8.732006e-215 9.999999e-01 8.322041e-08 5.804086e-140
## [185,] 5.859445e-06 5.617843e-101 5.390517e-50 9.999941e-01 0.000000e+00
## [186,] 3.757939e-156 0.000000e+00 1.493753e-18 1.000000e+00 9.913735e-112
## [187,] 4.790689e-278 0.000000e+00 6.320193e-08 9.724800e-31 5.819683e-28
## [188,] 1.969000e-60 0.000000e+00 1.436643e-56 1.322595e-01 0.000000e+00
## [189,] 1.269551e-37 0.000000e+00 4.512923e-65 9.990334e-01 0.000000e+00
## [190,] 0.000000e+00 0.000000e+00 3.453163e-22 3.178152e-84 5.967941e-16
## [191,] 1.616335e-299 0.000000e+00 9.999667e-01 1.090803e-32 9.420704e-60
## [192,] 8.545390e-54 2.362028e-28 1.000000e+00 9.172552e-11 1.805969e-62
## [193,] 3.637886e-146 0.000000e+00 9.999778e-01 1.307513e-16 1.190982e-51
## [194,] 0.000000e+00 0.000000e+00 1.910756e-31 4.430616e-141 2.861086e-22
## [195,] 0.000000e+00 0.000000e+00 3.415138e-146 2.906569e-31 1.389646e-42
## [196,] 3.369906e-110 0.000000e+00 1.861011e-15 9.999898e-01 1.374637e-143
## [197,] 0.000000e+00 0.000000e+00 2.570764e-26 4.378456e-100 1.951444e-14
## [198,] 0.000000e+00 0.000000e+00 1.047297e-04 6.504795e-25 9.683042e-73
## [199,] 1.455906e-52 3.293912e-30 3.433893e-18 3.831510e-08 0.000000e+00
## [200,] 7.112839e-216 0.000000e+00 1.368435e-06 7.971921e-20 5.201760e-37
## [201,] 0.000000e+00 0.000000e+00 2.970380e-31 8.936634e-117 8.461404e-55
## [202,] 0.000000e+00 0.000000e+00 1.201737e-10 8.864187e-104 5.832543e-72
## [203,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.000000e+00
## [204,] 1.000000e+00 2.244674e-224 2.741354e-187 1.140948e-43 0.000000e+00
## [205,] 3.438448e-276 0.000000e+00 1.741387e-10 3.520176e-26 3.921067e-21
## [206,] 0.000000e+00 0.000000e+00 7.663322e-10 9.102878e-225 1.080869e-128
## [207,] 3.533060e-06 3.192712e-78 1.392517e-43 1.337685e-05 0.000000e+00
## [208,] 2.162836e-79 2.803022e-01 2.338346e-01 4.079565e-11 5.771628e-64
## [209,] 7.967526e-166 9.217262e-292 3.778886e-05 9.999622e-01 1.284966e-67
## [210,] 3.358602e-260 2.566272e-02 9.132673e-01 4.989683e-41 1.666076e-35
## [211,] 6.118343e-100 2.178102e-16 1.000000e+00 5.264324e-12 1.195160e-44
## [212,] 3.584748e-197 9.999979e-01 4.152889e-07 4.934908e-35 5.750658e-54
## [213,] 8.331436e-46 0.000000e+00 4.044233e-27 1.000000e+00 0.000000e+00
## [214,] 7.712383e-136 0.000000e+00 1.000000e+00 3.857231e-10 1.664837e-81
## [215,] 4.414780e-21 1.295048e-15 2.363493e-13 1.987096e-07 1.641575e-101
## [216,] 4.308066e-88 9.783848e-47 2.563544e-32 1.656096e-14 0.000000e+00
## [217,] 4.855472e-207 9.999264e-01 7.016564e-07 4.901621e-35 2.032892e-19
## [218,] 5.855157e-18 2.324086e-40 2.426915e-30 3.675900e-07 4.497814e-295
## [219,] 0.000000e+00 0.000000e+00 3.742729e-28 4.017053e-77 4.559926e-25
## [220,] 1.000000e+00 9.220481e-239 1.094001e-179 2.041773e-25 0.000000e+00
## [221,] 1.852594e-07 7.153069e-64 1.882258e-48 6.100945e-09 0.000000e+00
## [222,] 9.997246e-01 1.209225e-85 5.212529e-39 2.752057e-04 2.850893e-297
## [223,] 7.293094e-132 1.908137e-81 8.349144e-48 6.021448e-29 0.000000e+00
## [224,] 5.324704e-07 8.983509e-57 1.697688e-36 4.467934e-03 0.000000e+00
## [225,] 1.105094e-132 1.555142e-39 9.999999e-01 1.978473e-32 5.185380e-32
## [226,] 8.313028e-270 1.000000e+00 5.531774e-09 1.946147e-43 7.518178e-50
## [227,] 9.968611e-140 0.000000e+00 1.000000e+00 1.933393e-17 3.956724e-82
## [228,] 7.869234e-83 1.220263e-27 3.082430e-29 3.937180e-16 1.038825e-164
## [229,] 9.025204e-19 1.698785e-62 2.043418e-14 1.000000e+00 1.187885e-163
## [230,] 2.263842e-243 9.993093e-01 1.641842e-08 1.917920e-47 4.632928e-12
## [231,] 8.809550e-316 9.999897e-01 2.973967e-07 2.286413e-39 1.785914e-57
## [232,] 1.454715e-87 9.421080e-07 2.430479e-05 1.713718e-11 1.364574e-159
## [233,] 6.594291e-32 8.064241e-47 1.176983e-35 2.526888e-09 0.000000e+00
## [234,] 1.540884e-19 1.673030e-66 8.604793e-24 1.000000e+00 0.000000e+00
## [235,] 1.829156e-164 1.835129e-26 5.558708e-18 1.805352e-15 0.000000e+00
## [236,] 1.863815e-98 1.184682e-04 2.261141e-04 1.182373e-15 7.163232e-78
## [237,] 8.297289e-92 1.124233e-20 1.845240e-13 1.504449e-10 1.156049e-303
## [238,] 1.000000e+00 7.297353e-282 9.487059e-184 1.528940e-36 0.000000e+00
## [239,] 1.654333e-47 1.472422e-35 1.051955e-28 2.934967e-11 8.398367e-242
## [240,] 5.909424e-11 4.206890e-65 3.153927e-37 6.514780e-08 1.693073e-278
## [241,] 0.000000e+00 5.444024e-109 7.147053e-16 1.045999e-70 5.746920e-18
## [242,] 4.341462e-70 1.467457e-09 1.381044e-10 1.352961e-12 4.838070e-104
## [243,] 1.027311e-208 7.173484e-22 1.225727e-17 3.236583e-21 0.000000e+00
## [244,] 0.000000e+00 1.000000e+00 7.989239e-14 5.941969e-60 1.853491e-46
## [245,] 3.940995e-41 3.113739e-25 1.021579e-16 5.850313e-05 1.504292e-84
## [246,] 3.250961e-51 8.621192e-01 4.069067e-06 3.762985e-14 1.752768e-49
## [247,] 2.494925e-242 3.576729e-06 3.425325e-04 3.425472e-18 2.726318e-74
## [248,] 0.000000e+00 0.000000e+00 7.299490e-316 4.089279e-35 0.000000e+00
## [249,] 1.000000e+00 0.000000e+00 0.000000e+00 5.126067e-154 0.000000e+00
## [250,] 0.000000e+00 1.000000e+00 3.320856e-20 2.077239e-71 2.891491e-29
## [251,] 1.701604e-38 2.128523e-21 1.508326e-18 6.064804e-08 5.353568e-156
## [252,] 0.000000e+00 8.183736e-13 3.178130e-08 5.744411e-39 1.255405e-132
## [253,] 0.000000e+00 9.997866e-85 1.000000e+00 1.656192e-20 2.720720e-73
## [254,] 1.000000e+00 0.000000e+00 0.000000e+00 4.005777e-104 0.000000e+00
## [255,] 8.056550e-272 1.000000e+00 3.672085e-08 1.418155e-37 5.586766e-43
## [256,] 1.974030e-237 1.000000e+00 1.668549e-14 6.884153e-43 2.303268e-72
## [257,] 1.175532e-136 1.174783e-160 9.999993e-01 1.038453e-21 8.076235e-43
## [258,] 1.108916e-224 9.946007e-01 3.523995e-03 3.186979e-36 1.042066e-24
## [259,] 1.408274e-46 1.857687e-320 4.168330e-10 1.000000e+00 1.244373e-82
## [260,] 2.499554e-62 1.008476e-08 9.164330e-08 3.609537e-08 4.303980e-74
## [261,] 1.000000e+00 7.674773e-166 4.946378e-126 8.794718e-28 0.000000e+00
## [262,] 1.279752e-31 2.994092e-85 1.208301e-79 6.370183e-23 0.000000e+00
## [263,] 1.451934e-84 7.696390e-11 6.336327e-11 8.386844e-10 2.092317e-118
## [264,] 1.192813e-34 1.358943e-16 2.034128e-10 4.784026e-04 1.222721e-82
## [265,] 1.572477e-225 0.000000e+00 1.623004e-05 1.091255e-20 2.293647e-14
## [266,] 3.071115e-94 1.564453e-01 1.120684e-02 5.365729e-09 1.073224e-46
## [267,] 0.000000e+00 0.000000e+00 3.012427e-10 1.385105e-118 2.190407e-19
## [268,] 2.849860e-148 0.000000e+00 9.950565e-01 3.964224e-15 2.852836e-19
## [269,] 0.000000e+00 0.000000e+00 6.381644e-22 1.517911e-69 2.223736e-19
## [270,] 2.742821e-283 8.109505e-03 9.918866e-01 8.455534e-31 4.679769e-85
## [271,] 1.000000e+00 0.000000e+00 1.016907e-284 7.228612e-67 0.000000e+00
## [272,] 0.000000e+00 1.000000e+00 4.887011e-17 4.225975e-59 1.150394e-41
## [273,] 0.000000e+00 1.000000e+00 2.044320e-13 7.728133e-58 2.217321e-48
## [274,] 1.000000e+00 0.000000e+00 5.314625e-272 2.080666e-37 0.000000e+00
## [275,] 0.000000e+00 0.000000e+00 1.155407e-93 6.593721e-141 1.000000e+00
## [276,] 1.618533e-14 1.614542e-59 3.742630e-66 5.374517e-19 0.000000e+00
## [277,] 0.000000e+00 4.046796e-48 1.252416e-35 1.394574e-32 1.253186e-160
## [278,] 8.641306e-137 1.000000e+00 1.037463e-11 3.177360e-29 9.931992e-58
## [279,] 9.590664e-27 4.401777e-22 5.723569e-17 2.129801e-08 1.014968e-188
## [280,] 3.601039e-59 0.000000e+00 2.314228e-16 1.000000e+00 8.897233e-150
## [281,] 0.000000e+00 0.000000e+00 9.425247e-31 1.207874e-27 6.290501e-17
## [282,] 5.762047e-312 6.559366e-74 6.032698e-10 1.741206e-49 4.637681e-20
## [283,] 9.414029e-01 8.500839e-149 6.318966e-69 2.607046e-02 0.000000e+00
## [284,] 2.461067e-40 7.540387e-34 3.386911e-30 3.734292e-12 2.460516e-247
## [285,] 1.276688e-123 5.197336e-25 3.136989e-28 6.082668e-31 1.944200e-261
## [286,] 1.618469e-201 1.255663e-180 1.000000e+00 2.016542e-36 9.784431e-35
## [287,] 1.000000e+00 0.000000e+00 5.367457e-236 1.129538e-39 0.000000e+00
## [288,] 1.000000e+00 0.000000e+00 3.605298e-253 1.911571e-46 0.000000e+00
## [289,] 4.158651e-164 1.000000e+00 2.959559e-11 4.313377e-41 4.868760e-13
## [290,] 1.095705e-15 7.269710e-119 1.092334e-66 1.964342e-10 0.000000e+00
## [291,] 7.322160e-82 1.693033e-16 3.039609e-13 9.184993e-14 2.337261e-149
## [292,] 5.967945e-275 9.817470e-31 9.461206e-30 3.393066e-37 0.000000e+00
## [293,] 3.006210e-12 1.637753e-118 1.527848e-76 4.956836e-09 0.000000e+00
## [294,] 3.923883e-27 3.128663e-73 3.476176e-64 1.015442e-15 0.000000e+00
## [295,] 0.000000e+00 7.975689e-01 1.307863e-08 2.229608e-34 1.150313e-59
## [296,] 4.434414e-95 1.979469e-13 1.000000e+00 4.612490e-20 3.639967e-31
## [297,] 7.847256e-56 1.954913e-11 1.632960e-04 1.212599e-05 1.088323e-61
## [298,] 1.712615e-103 3.325906e-13 8.671852e-07 6.384700e-10 2.210007e-116
## [299,] 2.057383e-230 9.999599e-01 5.442247e-08 1.024015e-27 1.437898e-51
## [300,] 1.175681e-24 3.930136e-55 2.960492e-39 3.587615e-09 0.000000e+00
## [301,] 2.532411e-86 4.696926e-13 5.080694e-06 1.604622e-06 4.340460e-161
## [302,] 0.000000e+00 0.000000e+00 8.627264e-15 1.522389e-64 9.999989e-01
## [303,] 2.771741e-293 1.000000e+00 1.602574e-11 3.508431e-46 2.628364e-42
## [304,] 1.158401e-109 5.287147e-18 1.000000e+00 9.897646e-10 3.788280e-79
## [305,] 1.000000e+00 2.258152e-173 8.453000e-115 9.105609e-24 0.000000e+00
## [306,] 1.000000e+00 3.354884e-274 1.285790e-177 5.542023e-31 0.000000e+00
## [307,] 0.000000e+00 2.512606e-06 7.006400e-10 1.681115e-70 5.740025e-22
## [308,] 0.000000e+00 9.998600e-01 1.094530e-13 1.440591e-63 2.602734e-83
## [309,] 5.155639e-37 2.698078e-220 3.065673e-11 1.000000e+00 4.113301e-164
## [310,] 1.492421e-143 9.921480e-01 8.191650e-05 6.727316e-20 4.481702e-70
## [311,] 2.207441e-226 1.112051e-07 9.998747e-01 2.286983e-28 1.923396e-30
## [312,] 7.687783e-16 9.463941e-25 1.326376e-08 9.983238e-01 8.701421e-123
## [313,] 1.047136e-07 1.149826e-34 3.714370e-26 9.999999e-01 2.245373e-305
## [314,] 1.465501e-30 4.244973e-93 1.661651e-47 1.965018e-12 0.000000e+00
## [315,] 4.672753e-208 9.999999e-01 2.560463e-08 8.775417e-32 4.093628e-48
## [316,] 7.526925e-79 9.740822e-11 8.124778e-10 2.467424e-13 4.535677e-69
## [317,] 9.999999e-01 2.454495e-123 1.920429e-72 9.922360e-10 0.000000e+00
## [318,] 5.362413e-240 9.999990e-01 9.889390e-07 4.978694e-24 1.552044e-65
## [319,] 3.604522e-182 1.000000e+00 5.649429e-17 5.850924e-35 7.338578e-72
## [320,] 1.414499e-57 3.100194e-38 4.035831e-33 1.697764e-07 0.000000e+00
## [321,] 2.214013e-20 5.088627e-38 2.782910e-52 8.497026e-16 6.916919e-322
## [322,] 0.000000e+00 3.261100e-83 1.207977e-60 3.818694e-44 1.873923e-152
## [323,] 2.100685e-42 1.280361e-25 5.253354e-24 1.066206e-13 2.282153e-151
## [324,] 1.000000e+00 2.989240e-169 8.977892e-135 1.245132e-26 0.000000e+00
## [325,] 0.000000e+00 1.673406e-33 2.997389e-42 6.646690e-89 0.000000e+00
## [326,] 2.549379e-321 1.000000e+00 6.870784e-11 2.088202e-46 7.383008e-41
## [327,] 1.000000e+00 5.890127e-156 1.610214e-84 4.124460e-14 0.000000e+00
## [328,] 8.037398e-77 8.101900e-20 5.153645e-18 7.870577e-12 6.472451e-266
## [329,] 2.400058e-30 0.000000e+00 1.134559e-103 1.000000e+00 6.786524e-308
## [330,] 9.339436e-15 1.125237e-278 1.962141e-114 5.381190e-20 0.000000e+00
## [331,] 1.162401e-67 2.517280e-06 9.275861e-18 2.653281e-15 1.983303e-145
## [332,] 1.224304e-93 1.123748e-09 2.692144e-09 1.264613e-11 6.980397e-132
## [333,] 0.000000e+00 1.000000e+00 1.262770e-15 5.455808e-60 2.051857e-48
## [334,] 3.897486e-253 9.999998e-01 3.523611e-17 1.358977e-36 3.187055e-83
## [335,] 0.000000e+00 1.000000e+00 6.151111e-27 7.525299e-87 1.375664e-41
## [336,] 3.880646e-37 1.685569e-36 1.211962e-28 3.563588e-12 0.000000e+00
## [337,] 9.354043e-15 2.506317e-62 7.878853e-51 1.462908e-09 0.000000e+00
## [338,] 1.228371e-52 3.706398e-02 1.087625e-03 7.140214e-17 1.752372e-64
## [339,] 0.000000e+00 3.126558e-104 4.132497e-95 1.652287e-83 1.253036e-228
## [340,] 3.217095e-59 3.197330e-25 2.762876e-18 6.578573e-13 0.000000e+00
## [341,] 1.000000e+00 8.294924e-141 3.695110e-94 2.293452e-18 0.000000e+00
## [342,] 1.070944e-51 4.075850e-14 1.363354e-18 9.304207e-13 1.662107e-181
## [343,] 1.000000e+00 8.929640e-268 1.479788e-187 2.931738e-24 0.000000e+00
## [344,] 1.000000e+00 0.000000e+00 0.000000e+00 5.878139e-84 0.000000e+00
## [345,] 5.410564e-247 9.437147e-202 5.536197e-06 7.853314e-35 9.075288e-15
## [346,] 9.999940e-01 1.691693e-105 4.029692e-85 8.101852e-18 0.000000e+00
## [347,] 6.500638e-69 2.438721e-41 2.166799e-33 5.585724e-10 0.000000e+00
## [348,] 5.148151e-70 3.130333e-06 1.893690e-09 1.581764e-14 2.456929e-87
## [349,] 8.139546e-43 0.000000e+00 2.671278e-18 1.000000e+00 7.593973e-286
## [350,] 1.860699e-114 4.751817e-40 9.061404e-16 3.761761e-06 0.000000e+00
## [351,] 0.000000e+00 9.998237e-01 8.047328e-11 3.518686e-37 3.722714e-61
## [352,] 1.000000e+00 0.000000e+00 0.000000e+00 3.513908e-195 0.000000e+00
## [353,] 0.000000e+00 1.000000e+00 3.804783e-23 6.630920e-92 5.991449e-43
## [354,] 5.077391e-93 1.342457e-04 3.132364e-08 2.872647e-12 1.264060e-72
## [355,] 1.409426e-113 0.000000e+00 5.003121e-15 1.000000e+00 5.629158e-109
## [356,] 9.998221e-01 1.917827e-153 1.068174e-96 6.230757e-08 0.000000e+00
## [357,] 0.000000e+00 1.000000e+00 5.897731e-18 1.659088e-60 6.839715e-31
## [358,] 1.081129e-150 1.000000e+00 1.994504e-10 2.352084e-27 5.876573e-70
## [359,] 4.182468e-41 2.115642e-21 2.982147e-23 2.252172e-11 1.672817e-264
## [360,] 0.000000e+00 0.000000e+00 0.000000e+00 1.611714e-92 4.432785e-30
## [361,] 6.726730e-104 1.433470e-09 1.249371e-12 9.087214e-12 3.811104e-105
## [362,] 6.351242e-33 2.540146e-20 4.044739e-14 1.389787e-09 3.182362e-159
## [363,] 1.137992e-196 0.000000e+00 9.995169e-01 6.619900e-18 7.441899e-24
## [364,] 3.302497e-76 0.000000e+00 9.992191e-01 4.855121e-06 1.333277e-66
## [365,] 8.758422e-187 0.000000e+00 9.999997e-01 7.452342e-27 3.661991e-59
## [366,] 1.572528e-275 8.877603e-01 1.328692e-07 1.300952e-33 9.642636e-139
## [367,] 8.656029e-09 6.030986e-104 3.734363e-70 9.791571e-12 0.000000e+00
## [368,] 1.092666e-101 9.999934e-01 6.856148e-08 2.655948e-22 3.434626e-50
## [369,] 5.062405e-141 0.000000e+00 2.788725e-34 9.998166e-01 1.935337e-258
## [370,] 1.669062e-22 7.600874e-75 3.630482e-49 6.597657e-10 0.000000e+00
## [371,] 1.000000e+00 4.940656e-324 5.747297e-152 3.403213e-20 0.000000e+00
## [372,] 8.152297e-102 9.997191e-01 5.033013e-05 1.535933e-21 9.635685e-37
## [373,] 2.474651e-240 1.000000e+00 3.370689e-16 1.257047e-45 2.983824e-70
## [374,] 0.000000e+00 0.000000e+00 2.971606e-126 3.686516e-157 1.000000e+00
## [375,] 5.393066e-220 1.000000e+00 1.344991e-15 8.448823e-36 7.353852e-83
## [376,] 1.865734e-35 7.632395e-35 2.087202e-53 7.879655e-20 4.940656e-324
## [377,] 2.754500e-86 1.615192e-03 3.113535e-07 9.666617e-18 3.505036e-60
## class-6 class-7 class-8 class-9 class-10
## [1,] 1.619032e-30 1.406433e-72 1.187806e-10 3.775086e-13 4.807062e-18
## [2,] 0.000000e+00 3.410110e-112 0.000000e+00 1.000000e+00 4.295330e-09
## [3,] 0.000000e+00 4.066389e-14 0.000000e+00 1.305572e-61 1.000000e+00
## [4,] 7.798479e-291 3.211766e-34 5.573060e-321 1.296766e-04 3.582293e-04
## [5,] 0.000000e+00 5.857658e-66 0.000000e+00 7.147803e-56 2.313656e-24
## [6,] 0.000000e+00 1.864829e-77 0.000000e+00 3.350734e-123 1.000000e+00
## [7,] 1.000000e+00 4.596635e-18 8.582051e-17 6.921234e-165 1.547452e-106
## [8,] 0.000000e+00 3.154126e-19 0.000000e+00 9.394232e-46 1.000000e+00
## [9,] 0.000000e+00 1.385527e-45 0.000000e+00 1.674995e-06 9.999983e-01
## [10,] 0.000000e+00 2.271676e-10 0.000000e+00 3.122887e-23 9.999979e-01
## [11,] 0.000000e+00 4.292578e-44 0.000000e+00 3.360238e-17 1.000000e+00
## [12,] 0.000000e+00 2.375030e-213 0.000000e+00 4.303877e-80 1.000000e+00
## [13,] 1.101751e-30 1.102857e-22 1.000000e+00 5.841985e-314 2.972788e-29
## [14,] 0.000000e+00 1.000000e+00 1.256399e-98 9.460818e-86 1.963148e-18
## [15,] 0.000000e+00 1.000000e+00 0.000000e+00 3.105910e-253 8.635323e-68
## [16,] 0.000000e+00 1.148094e-63 0.000000e+00 9.942823e-01 9.438312e-04
## [17,] 0.000000e+00 2.489999e-156 0.000000e+00 2.232625e-39 1.000000e+00
## [18,] 0.000000e+00 4.173496e-61 0.000000e+00 1.000000e+00 1.145727e-10
## [19,] 0.000000e+00 4.336557e-51 0.000000e+00 9.992505e-01 2.614603e-06
## [20,] 0.000000e+00 3.095094e-55 0.000000e+00 1.033403e-04 9.998967e-01
## [21,] 0.000000e+00 3.436197e-10 0.000000e+00 3.746920e-115 9.741857e-26
## [22,] 0.000000e+00 2.807328e-43 0.000000e+00 5.874708e-03 9.941253e-01
## [23,] 0.000000e+00 9.119517e-56 0.000000e+00 9.998958e-01 1.187679e-11
## [24,] 2.836497e-02 8.533663e-21 6.532147e-05 2.064538e-26 1.762104e-10
## [25,] 0.000000e+00 9.508667e-42 0.000000e+00 9.999983e-01 5.497305e-10
## [26,] 0.000000e+00 4.816234e-27 0.000000e+00 6.807457e-09 2.811813e-03
## [27,] 5.533993e-16 1.312671e-41 7.809644e-15 8.298408e-09 1.648199e-17
## [28,] 0.000000e+00 3.519831e-108 0.000000e+00 4.239706e-57 3.900996e-61
## [29,] 9.727554e-20 9.630275e-52 3.485304e-12 1.142759e-21 2.285727e-34
## [30,] 7.540759e-47 8.559275e-37 1.221184e-48 1.065965e-05 1.349388e-07
## [31,] 0.000000e+00 1.278200e-23 0.000000e+00 1.963201e-03 9.915105e-01
## [32,] 0.000000e+00 1.814946e-04 0.000000e+00 3.105978e-67 7.448747e-12
## [33,] 0.000000e+00 2.599968e-56 0.000000e+00 9.999999e-01 3.349403e-08
## [34,] 5.173292e-24 5.700174e-55 1.759612e-07 6.567219e-25 4.188121e-23
## [35,] 0.000000e+00 7.815816e-27 0.000000e+00 4.169304e-33 1.809311e-09
## [36,] 0.000000e+00 1.962785e-60 0.000000e+00 1.000000e+00 1.754569e-13
## [37,] 0.000000e+00 5.974384e-67 0.000000e+00 1.000000e+00 1.894021e-12
## [38,] 2.800722e-14 2.196540e-14 2.790136e-07 1.712221e-37 1.818836e-09
## [39,] 1.671695e-24 8.116171e-57 6.051510e-11 2.477211e-12 3.004716e-12
## [40,] 0.000000e+00 5.015540e-28 0.000000e+00 2.933913e-36 1.397361e-08
## [41,] 0.000000e+00 1.937345e-29 0.000000e+00 3.294620e-40 1.000000e+00
## [42,] 0.000000e+00 1.000000e+00 0.000000e+00 0.000000e+00 6.131234e-71
## [43,] 0.000000e+00 1.000000e+00 0.000000e+00 0.000000e+00 1.244938e-60
## [44,] 0.000000e+00 1.315741e-35 0.000000e+00 9.964649e-01 2.134814e-06
## [45,] 5.571311e-87 4.131407e-58 9.560629e-54 5.319421e-18 1.957348e-05
## [46,] 0.000000e+00 1.476193e-54 0.000000e+00 2.821089e-16 1.892459e-18
## [47,] 0.000000e+00 1.973525e-28 0.000000e+00 1.546172e-08 9.883662e-01
## [48,] 1.032391e-05 4.947152e-23 9.002256e-01 8.480661e-17 3.027215e-13
## [49,] 2.703417e-110 2.946572e-38 1.173462e-129 1.844453e-24 1.543046e-10
## [50,] 0.000000e+00 2.653507e-128 0.000000e+00 3.052302e-91 1.000000e+00
## [51,] 7.143387e-10 1.027667e-21 3.285531e-09 2.956378e-17 7.242847e-08
## [52,] 0.000000e+00 9.130233e-72 0.000000e+00 9.996420e-01 6.848410e-08
## [53,] 2.646177e-27 2.261925e-15 5.278124e-16 7.162248e-286 1.108291e-148
## [54,] 0.000000e+00 1.000000e+00 0.000000e+00 2.044359e-247 5.610370e-96
## [55,] 7.813867e-10 7.207281e-11 5.708070e-09 5.628181e-74 6.108691e-20
## [56,] 0.000000e+00 7.896171e-13 0.000000e+00 1.890731e-53 5.760824e-16
## [57,] 2.097184e-48 1.220702e-79 1.128425e-18 4.275195e-11 8.709290e-20
## [58,] 8.202026e-03 1.830045e-26 9.917980e-01 1.526556e-95 1.909202e-43
## [59,] 0.000000e+00 8.336227e-38 0.000000e+00 2.014066e-12 2.065321e-07
## [60,] 0.000000e+00 6.534859e-16 0.000000e+00 2.285644e-35 7.381626e-08
## [61,] 0.000000e+00 1.620022e-19 0.000000e+00 1.101031e-50 1.030979e-09
## [62,] 0.000000e+00 1.586957e-36 0.000000e+00 5.566496e-13 1.000000e+00
## [63,] 7.260659e-11 1.278927e-37 3.624246e-03 3.488666e-16 5.465815e-14
## [64,] 5.108892e-44 1.638629e-36 3.526601e-42 3.957976e-05 1.332548e-08
## [65,] 1.244543e-15 1.414365e-29 2.654045e-11 1.943026e-20 6.185422e-27
## [66,] 0.000000e+00 4.711152e-06 0.000000e+00 8.428368e-102 2.497433e-29
## [67,] 0.000000e+00 2.925343e-32 0.000000e+00 8.588493e-16 4.673338e-09
## [68,] 9.999999e-01 3.735218e-15 1.849788e-10 0.000000e+00 2.894600e-145
## [69,] 0.000000e+00 4.576521e-29 0.000000e+00 1.327908e-24 1.140160e-08
## [70,] 9.943462e-01 1.314212e-05 5.539834e-16 0.000000e+00 6.231359e-121
## [71,] 9.999972e-01 8.419688e-15 2.812453e-06 0.000000e+00 1.828289e-87
## [72,] 0.000000e+00 1.000000e+00 0.000000e+00 6.242657e-304 8.411334e-20
## [73,] 9.999998e-01 6.094647e-18 2.163489e-07 3.231329e-74 7.533277e-33
## [74,] 0.000000e+00 2.136195e-147 0.000000e+00 0.000000e+00 0.000000e+00
## [75,] 0.000000e+00 1.347941e-08 0.000000e+00 3.615340e-50 1.000000e+00
## [76,] 0.000000e+00 1.000000e+00 0.000000e+00 0.000000e+00 2.378879e-313
## [77,] 4.191286e-144 4.257137e-228 2.379655e-30 4.442075e-19 1.032366e-35
## [78,] 5.516984e-13 5.543383e-36 1.656890e-09 7.034739e-13 8.703390e-13
## [79,] 2.550422e-12 7.998571e-36 1.681924e-08 5.981653e-13 3.053836e-15
## [80,] 0.000000e+00 7.994641e-105 0.000000e+00 8.329952e-28 8.955855e-52
## [81,] 0.000000e+00 1.192021e-54 0.000000e+00 9.908726e-01 9.127397e-03
## [82,] 0.000000e+00 7.431626e-103 0.000000e+00 1.000000e+00 1.174269e-10
## [83,] 4.022746e-55 8.366459e-13 3.007570e-190 0.000000e+00 8.149547e-310
## [84,] 0.000000e+00 4.178013e-31 0.000000e+00 1.880000e-04 6.432163e-03
## [85,] 5.566800e-18 3.980800e-27 1.576297e-06 1.114994e-13 2.415769e-07
## [86,] 2.388250e-20 6.680942e-09 2.683958e-08 2.014388e-131 5.727807e-37
## [87,] 9.960622e-01 2.946387e-07 3.144048e-14 7.947168e-259 5.329297e-98
## [88,] 0.000000e+00 4.717484e-32 0.000000e+00 3.715178e-02 5.295947e-07
## [89,] 0.000000e+00 2.706232e-121 0.000000e+00 7.510009e-38 4.062500e-09
## [90,] 1.118369e-05 3.930153e-08 8.951133e-23 1.016075e-311 2.283943e-91
## [91,] 0.000000e+00 7.181628e-16 0.000000e+00 2.221171e-18 2.460346e-09
## [92,] 9.807290e-100 3.064337e-09 4.604617e-226 0.000000e+00 5.852820e-174
## [93,] 0.000000e+00 1.370665e-34 0.000000e+00 9.846700e-01 1.532996e-02
## [94,] 0.000000e+00 5.076643e-08 0.000000e+00 0.000000e+00 0.000000e+00
## [95,] 6.636151e-108 3.723598e-61 1.000000e+00 2.557423e-124 1.399674e-167
## [96,] 1.602794e-179 2.095565e-26 1.483599e-224 6.107598e-15 5.506710e-08
## [97,] 2.676872e-27 1.718459e-09 2.611269e-41 0.000000e+00 4.035513e-172
## [98,] 0.000000e+00 1.052562e-95 0.000000e+00 1.172554e-02 9.882745e-01
## [99,] 0.000000e+00 3.791857e-81 0.000000e+00 9.999998e-01 1.556240e-07
## [100,] 2.645808e-11 1.955070e-32 1.000000e+00 0.000000e+00 1.018321e-50
## [101,] 9.999650e-01 1.349987e-11 1.089716e-12 0.000000e+00 1.150831e-113
## [102,] 1.610549e-179 1.000000e+00 6.588861e-83 1.165995e-321 2.012144e-82
## [103,] 0.000000e+00 5.054505e-34 0.000000e+00 6.660523e-08 1.091119e-04
## [104,] 0.000000e+00 2.872921e-20 0.000000e+00 5.183085e-09 3.148340e-05
## [105,] 2.057073e-07 1.850484e-08 7.973396e-06 1.281475e-253 2.869437e-61
## [106,] 0.000000e+00 2.700079e-34 0.000000e+00 1.052394e-06 9.999989e-01
## [107,] 0.000000e+00 1.900432e-09 0.000000e+00 2.244433e-155 1.000000e+00
## [108,] 0.000000e+00 7.876478e-21 0.000000e+00 4.888214e-14 9.992937e-01
## [109,] 1.086705e-24 9.499784e-01 1.792894e-19 2.665939e-188 9.137591e-63
## [110,] 0.000000e+00 2.392298e-08 0.000000e+00 3.504436e-65 9.933830e-22
## [111,] 4.384569e-15 1.813732e-24 9.999987e-01 1.204273e-36 6.202564e-19
## [112,] 0.000000e+00 1.210007e-39 0.000000e+00 9.999924e-01 1.886853e-09
## [113,] 0.000000e+00 2.377870e-10 0.000000e+00 1.281360e-96 1.392186e-21
## [114,] 0.000000e+00 2.909203e-26 0.000000e+00 2.974794e-16 6.893921e-10
## [115,] 8.336470e-100 1.685235e-10 4.747538e-172 0.000000e+00 1.990404e-151
## [116,] 1.000000e+00 3.901346e-10 1.200623e-13 5.297868e-203 3.801928e-90
## [117,] 0.000000e+00 2.460163e-65 0.000000e+00 9.984569e-01 1.039875e-16
## [118,] 7.188496e-51 7.404549e-03 9.039407e-22 1.070227e-240 1.222337e-65
## [119,] 0.000000e+00 2.698695e-74 0.000000e+00 1.000000e+00 1.205557e-15
## [120,] 0.000000e+00 2.527944e-62 0.000000e+00 1.000000e+00 3.219400e-14
## [121,] 0.000000e+00 4.465083e-94 0.000000e+00 1.292535e-02 1.001235e-05
## [122,] 6.352250e-09 5.350256e-34 1.000000e+00 3.331846e-305 4.179445e-48
## [123,] 5.870919e-18 7.020241e-48 1.000000e+00 6.687351e-73 4.155047e-52
## [124,] 1.477401e-17 3.152254e-20 2.397919e-01 5.787208e-32 4.658229e-13
## [125,] 9.978724e-01 3.320305e-11 2.061645e-20 2.429551e-250 2.806425e-54
## [126,] 0.000000e+00 1.768710e-21 0.000000e+00 6.767337e-18 6.489959e-08
## [127,] 0.000000e+00 8.334827e-50 0.000000e+00 3.038575e-04 9.996961e-01
## [128,] 1.355859e-47 8.368402e-90 3.454646e-13 1.099952e-20 5.940863e-25
## [129,] 9.999774e-01 2.625956e-13 3.650621e-09 2.695382e-148 7.941329e-70
## [130,] 0.000000e+00 1.204045e-31 0.000000e+00 1.232175e-07 9.999999e-01
## [131,] 0.000000e+00 9.995998e-01 0.000000e+00 3.890216e-178 5.796857e-45
## [132,] 0.000000e+00 1.143447e-02 0.000000e+00 7.318425e-214 4.299371e-59
## [133,] 0.000000e+00 4.437704e-29 0.000000e+00 9.843119e-01 4.752406e-03
## [134,] 0.000000e+00 2.245932e-81 0.000000e+00 0.000000e+00 0.000000e+00
## [135,] 4.310783e-232 1.216318e-46 1.757931e-317 0.000000e+00 0.000000e+00
## [136,] 0.000000e+00 8.173464e-40 0.000000e+00 4.511796e-13 3.809842e-13
## [137,] 0.000000e+00 1.000000e+00 0.000000e+00 2.326681e-255 2.346579e-44
## [138,] 0.000000e+00 2.896874e-08 0.000000e+00 2.571099e-44 1.540589e-09
## [139,] 0.000000e+00 2.494721e-68 0.000000e+00 9.999987e-01 1.317387e-06
## [140,] 2.827738e-01 4.279650e-21 7.172231e-01 1.761116e-67 2.539242e-28
## [141,] 0.000000e+00 2.999127e-03 0.000000e+00 4.476056e-144 1.741016e-35
## [142,] 0.000000e+00 7.502797e-16 0.000000e+00 2.219759e-35 1.366314e-12
## [143,] 0.000000e+00 1.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [144,] 4.825004e-12 8.536558e-36 2.194224e-04 2.961427e-16 1.378957e-09
## [145,] 0.000000e+00 5.807721e-10 0.000000e+00 2.280892e-60 1.979183e-16
## [146,] 1.481133e-13 2.346731e-20 9.999903e-01 6.052859e-82 9.262900e-21
## [147,] 0.000000e+00 8.871805e-69 0.000000e+00 1.000000e+00 7.441309e-10
## [148,] 0.000000e+00 2.105433e-80 0.000000e+00 1.314561e-13 4.776941e-07
## [149,] 9.999986e-01 2.496724e-11 2.249083e-16 3.040090e-103 7.306424e-44
## [150,] 2.210140e-02 2.005052e-23 9.631812e-01 8.821095e-45 6.108478e-16
## [151,] 2.723611e-15 2.881922e-05 8.041018e-29 1.793929e-174 5.450129e-56
## [152,] 0.000000e+00 4.206874e-26 0.000000e+00 2.222905e-16 1.000000e+00
## [153,] 0.000000e+00 1.000000e+00 0.000000e+00 3.071154e-265 2.267033e-61
## [154,] 0.000000e+00 3.180891e-93 0.000000e+00 9.999998e-01 2.676190e-11
## [155,] 0.000000e+00 2.869975e-12 0.000000e+00 1.282859e-97 9.366270e-30
## [156,] 0.000000e+00 2.521580e-85 0.000000e+00 1.987208e-17 1.591932e-25
## [157,] 9.999130e-01 3.277571e-09 8.678923e-05 3.616550e-245 5.970057e-74
## [158,] 1.043070e-73 1.685028e-23 1.192093e-27 6.702866e-133 3.318216e-90
## [159,] 0.000000e+00 2.356327e-15 0.000000e+00 3.555499e-300 6.094135e-31
## [160,] 0.000000e+00 3.952269e-64 0.000000e+00 2.344279e-07 5.079695e-04
## [161,] 0.000000e+00 7.451011e-12 0.000000e+00 4.009283e-68 3.771851e-17
## [162,] 7.372166e-22 2.908388e-13 1.097501e-48 0.000000e+00 8.024442e-266
## [163,] 0.000000e+00 7.509086e-31 0.000000e+00 9.885380e-16 1.000000e+00
## [164,] 0.000000e+00 5.157651e-70 0.000000e+00 9.999890e-01 1.764582e-11
## [165,] 0.000000e+00 2.433106e-102 0.000000e+00 7.120485e-101 1.000000e+00
## [166,] 0.000000e+00 6.365840e-266 3.223856e-132 0.000000e+00 0.000000e+00
## [167,] 0.000000e+00 3.593755e-55 0.000000e+00 9.124797e-14 3.782137e-04
## [168,] 0.000000e+00 1.000000e+00 0.000000e+00 0.000000e+00 2.961540e-87
## [169,] 0.000000e+00 3.425795e-187 0.000000e+00 2.778171e-12 1.000000e+00
## [170,] 0.000000e+00 2.272777e-29 0.000000e+00 2.820794e-44 1.000000e+00
## [171,] 0.000000e+00 1.042724e-17 0.000000e+00 1.063111e-26 4.496629e-03
## [172,] 0.000000e+00 5.650625e-41 0.000000e+00 9.999779e-01 2.734483e-06
## [173,] 0.000000e+00 9.431003e-45 0.000000e+00 9.997075e-01 3.254596e-11
## [174,] 0.000000e+00 1.573642e-55 0.000000e+00 6.377619e-03 5.173449e-07
## [175,] 9.999997e-01 1.348181e-17 2.597193e-07 0.000000e+00 2.981047e-54
## [176,] 0.000000e+00 1.245606e-17 0.000000e+00 6.119700e-39 1.027822e-10
## [177,] 0.000000e+00 7.334222e-05 0.000000e+00 3.664415e-65 6.279021e-06
## [178,] 0.000000e+00 2.745852e-33 0.000000e+00 4.079799e-21 1.000000e+00
## [179,] 0.000000e+00 1.000000e+00 0.000000e+00 1.129380e-216 6.373907e-39
## [180,] 1.577519e-09 6.683686e-24 3.812560e-04 2.965100e-28 8.916881e-10
## [181,] 0.000000e+00 1.616170e-22 0.000000e+00 2.081298e-19 1.000000e+00
## [182,] 5.260808e-09 6.335665e-25 9.998283e-01 2.217701e-43 2.470005e-19
## [183,] 0.000000e+00 1.315702e-45 0.000000e+00 1.233661e-15 2.521147e-14
## [184,] 0.000000e+00 2.151128e-32 0.000000e+00 5.940225e-46 1.763951e-14
## [185,] 2.073034e-10 1.153528e-09 1.103538e-09 0.000000e+00 2.119188e-82
## [186,] 0.000000e+00 1.572628e-10 0.000000e+00 2.232146e-59 1.074202e-16
## [187,] 0.000000e+00 5.371028e-32 0.000000e+00 1.446431e-05 9.999855e-01
## [188,] 0.000000e+00 8.677405e-01 0.000000e+00 7.859106e-307 2.173468e-63
## [189,] 0.000000e+00 9.665857e-04 0.000000e+00 0.000000e+00 4.194940e-83
## [190,] 0.000000e+00 4.310528e-58 0.000000e+00 9.999998e-01 2.346785e-07
## [191,] 0.000000e+00 5.755697e-50 0.000000e+00 1.358476e-14 3.330408e-05
## [192,] 1.137710e-201 3.665190e-20 1.283962e-272 2.763112e-25 4.553295e-13
## [193,] 0.000000e+00 2.288498e-30 0.000000e+00 4.452114e-10 2.224193e-05
## [194,] 0.000000e+00 9.606696e-131 0.000000e+00 1.449381e-02 9.855062e-01
## [195,] 0.000000e+00 9.997086e-01 0.000000e+00 3.145043e-98 2.913642e-04
## [196,] 0.000000e+00 1.024494e-05 0.000000e+00 2.196555e-53 4.215402e-11
## [197,] 0.000000e+00 1.708886e-64 0.000000e+00 2.792372e-02 9.720763e-01
## [198,] 0.000000e+00 2.073300e-38 0.000000e+00 2.218296e-27 9.998953e-01
## [199,] 7.905080e-01 9.938687e-22 2.094920e-01 2.444912e-283 6.863938e-35
## [200,] 0.000000e+00 7.228535e-21 0.000000e+00 4.876822e-13 9.999986e-01
## [201,] 0.000000e+00 2.189083e-88 0.000000e+00 6.688893e-15 1.000000e+00
## [202,] 0.000000e+00 3.919764e-125 0.000000e+00 6.049681e-16 1.000000e+00
## [203,] 0.000000e+00 1.303541e-147 0.000000e+00 0.000000e+00 9.545726e-304
## [204,] 8.686805e-12 7.810384e-25 5.701763e-30 0.000000e+00 0.000000e+00
## [205,] 0.000000e+00 4.031649e-24 0.000000e+00 9.369892e-07 9.999991e-01
## [206,] 0.000000e+00 6.654766e-165 0.000000e+00 7.400635e-48 1.000000e+00
## [207,] 9.999831e-01 1.248135e-09 5.406634e-11 1.518175e-187 5.399746e-78
## [208,] 1.083808e-02 5.260182e-25 4.750251e-01 9.382518e-20 5.004363e-10
## [209,] 0.000000e+00 8.636539e-25 0.000000e+00 1.320602e-24 2.341271e-24
## [210,] 8.888910e-17 2.269031e-46 6.106996e-02 4.503814e-09 2.581018e-09
## [211,] 1.668659e-126 3.260041e-25 2.711789e-168 7.194112e-15 1.024569e-09
## [212,] 7.381099e-16 3.152003e-44 1.725054e-06 7.685596e-20 4.601057e-17
## [213,] 0.000000e+00 9.015109e-09 0.000000e+00 1.489088e-161 3.218791e-43
## [214,] 0.000000e+00 5.011651e-25 0.000000e+00 1.370076e-34 3.086050e-10
## [215,] 9.999996e-01 1.662409e-16 1.679178e-07 7.611946e-50 7.181496e-24
## [216,] 5.539305e-03 4.453580e-22 9.944607e-01 6.377858e-204 3.194169e-78
## [217,] 3.016321e-52 1.051099e-42 3.440640e-55 7.287712e-05 4.389562e-13
## [218,] 9.999996e-01 4.439015e-14 7.610973e-14 2.908168e-108 1.031045e-46
## [219,] 0.000000e+00 3.387968e-49 0.000000e+00 1.636565e-12 1.000000e+00
## [220,] 6.505703e-24 3.408690e-22 3.223956e-55 0.000000e+00 8.377662e-269
## [221,] 9.999998e-01 6.524535e-16 8.557912e-12 0.000000e+00 3.705847e-77
## [222,] 4.283859e-09 1.461698e-07 3.029050e-21 1.529691e-135 4.467064e-52
## [223,] 8.354077e-04 6.770342e-11 9.991646e-01 0.000000e+00 2.856471e-77
## [224,] 9.955315e-01 3.970275e-10 1.391238e-14 1.124188e-178 1.278994e-47
## [225,] 0.000000e+00 9.202171e-25 0.000000e+00 7.986062e-08 5.635862e-08
## [226,] 2.123874e-23 5.642159e-56 6.250069e-12 1.735710e-17 3.922482e-16
## [227,] 0.000000e+00 2.258892e-29 0.000000e+00 3.050495e-26 1.063663e-08
## [228,] 2.862867e-21 7.160833e-32 1.000000e+00 3.532211e-83 3.373585e-76
## [229,] 2.252844e-20 2.473593e-09 1.362312e-10 7.988280e-72 8.180266e-21
## [230,] 4.584587e-42 1.351122e-50 8.263545e-36 6.906689e-04 2.372082e-12
## [231,] 6.737350e-22 5.579150e-58 9.971199e-06 3.239911e-16 1.720041e-17
## [232,] 8.855810e-01 5.115856e-24 1.143937e-01 3.668869e-52 8.926109e-19
## [233,] 1.000000e+00 7.180280e-17 1.961707e-08 1.123322e-144 1.064857e-77
## [234,] 2.934620e-18 2.612931e-08 7.190032e-16 7.656592e-108 1.071041e-26
## [235,] 2.728154e-11 4.451895e-35 1.000000e+00 1.815432e-194 3.911485e-39
## [236,] 9.934910e-01 3.160818e-23 6.164400e-03 2.901715e-28 5.126658e-15
## [237,] 9.999992e-01 7.647646e-22 7.975913e-07 3.639799e-88 2.150665e-28
## [238,] 1.212853e-29 4.660489e-14 9.171622e-63 0.000000e+00 1.499625e-276
## [239,] 9.999902e-01 7.324636e-20 9.823711e-06 3.914081e-119 2.487846e-67
## [240,] 9.999999e-01 4.666738e-11 1.490973e-13 1.257381e-162 1.163196e-72
## [241,] 0.000000e+00 7.519220e-81 0.000000e+00 1.000000e+00 3.484691e-10
## [242,] 9.999293e-01 3.576689e-23 7.065033e-05 9.407684e-43 2.494790e-30
## [243,] 1.818124e-10 9.183144e-42 1.000000e+00 4.353846e-195 5.670186e-42
## [244,] 2.259547e-34 1.204526e-80 1.689264e-09 4.576999e-17 3.193769e-19
## [245,] 1.481348e-13 1.171396e-18 9.999415e-01 5.948071e-46 1.349590e-32
## [246,] 7.681665e-02 1.410898e-22 6.106008e-02 9.898687e-23 1.432678e-21
## [247,] 1.701207e-10 2.266620e-42 9.996539e-01 2.152558e-32 4.464923e-17
## [248,] 0.000000e+00 1.000000e+00 0.000000e+00 0.000000e+00 1.101822e-147
## [249,] 0.000000e+00 5.307588e-37 0.000000e+00 0.000000e+00 0.000000e+00
## [250,] 1.169804e-38 7.531706e-77 7.661613e-17 1.318352e-12 4.744900e-27
## [251,] 9.999999e-01 8.395120e-18 8.156070e-10 7.864124e-71 3.035990e-33
## [252,] 6.667083e-29 1.207554e-69 1.000000e+00 4.757610e-33 1.289839e-19
## [253,] 0.000000e+00 7.361731e-46 0.000000e+00 1.772838e-20 5.345911e-11
## [254,] 0.000000e+00 3.359238e-69 0.000000e+00 0.000000e+00 0.000000e+00
## [255,] 5.289676e-21 3.735839e-53 1.321130e-08 6.433560e-13 4.618315e-15
## [256,] 2.574042e-20 4.937381e-49 1.241341e-12 1.037831e-32 5.290881e-41
## [257,] 0.000000e+00 1.051142e-28 0.000000e+00 1.354507e-13 7.378367e-07
## [258,] 7.809097e-53 5.202523e-49 5.580588e-51 1.875350e-03 2.781366e-12
## [259,] 0.000000e+00 3.932701e-10 0.000000e+00 1.053225e-36 4.294855e-13
## [260,] 9.999182e-01 7.011201e-21 8.168691e-05 1.391127e-31 2.843895e-16
## [261,] 9.654370e-25 8.405668e-14 3.474986e-50 0.000000e+00 3.031476e-246
## [262,] 1.000000e+00 2.314507e-15 4.408010e-13 3.769561e-290 2.888528e-167
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## [268,] 0.000000e+00 1.135210e-24 0.000000e+00 1.810630e-04 4.762426e-03
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## [273,] 9.967231e-32 3.575015e-64 7.333617e-14 7.406348e-14 5.466735e-20
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## [276,] 1.000000e+00 6.391639e-17 1.862009e-10 2.974531e-242 9.401983e-155
## [277,] 4.959974e-55 2.191121e-71 1.000000e+00 2.998591e-61 7.924903e-67
## [278,] 8.320884e-14 1.086200e-42 4.619727e-09 6.526584e-25 2.631138e-27
## [279,] 9.999997e-01 1.384746e-17 2.453787e-07 3.577363e-78 2.354021e-33
## [280,] 0.000000e+00 9.358888e-10 0.000000e+00 2.889666e-65 8.009377e-26
## [281,] 0.000000e+00 6.153322e-16 0.000000e+00 5.272189e-22 1.000000e+00
## [282,] 0.000000e+00 9.551962e-52 0.000000e+00 9.989054e-01 1.094622e-03
## [283,] 1.457406e-14 3.252659e-02 3.053522e-31 3.153539e-290 5.031097e-84
## [284,] 1.000000e+00 2.181183e-20 2.332037e-19 2.031066e-103 1.661387e-68
## [285,] 5.295365e-08 2.497964e-30 9.999999e-01 1.427338e-156 4.293105e-74
## [286,] 0.000000e+00 1.477076e-32 0.000000e+00 6.126547e-10 8.782169e-09
## [287,] 1.276473e-81 7.229263e-13 1.614889e-222 0.000000e+00 1.293628e-309
## [288,] 3.816445e-41 6.997981e-18 2.599206e-85 0.000000e+00 0.000000e+00
## [289,] 1.636961e-20 1.374020e-42 5.124071e-15 4.431838e-09 1.985223e-18
## [290,] 1.000000e+00 5.380456e-12 1.590154e-40 3.593652e-233 3.768321e-89
## [291,] 9.990735e-01 1.838519e-21 9.265162e-04 1.104256e-60 1.676888e-35
## [292,] 1.937151e-13 6.006371e-49 1.000000e+00 1.579474e-266 2.104382e-73
## [293,] 1.000000e+00 9.021085e-15 4.444957e-10 0.000000e+00 5.758606e-127
## [294,] 1.000000e+00 2.518303e-14 4.249793e-12 9.730986e-214 8.307026e-139
## [295,] 2.245567e-19 1.415107e-59 2.024311e-01 2.710265e-25 1.035338e-18
## [296,] 4.058387e-148 2.056966e-24 1.928470e-201 5.081422e-12 9.646939e-13
## [297,] 9.995279e-01 6.322647e-18 2.966754e-04 5.887951e-27 5.687122e-14
## [298,] 4.590156e-07 6.686018e-25 9.999987e-01 2.663489e-40 1.228337e-18
## [299,] 9.788380e-13 2.570826e-45 4.009390e-05 8.236796e-21 4.508603e-22
## [300,] 1.000000e+00 9.078943e-20 3.708059e-09 0.000000e+00 3.567366e-67
## [301,] 2.037125e-01 1.233063e-21 7.962808e-01 6.571396e-51 3.457493e-15
## [302,] 0.000000e+00 1.909212e-52 0.000000e+00 1.065256e-06 1.798062e-22
## [303,] 1.077445e-23 2.087988e-55 4.097869e-13 2.257461e-16 7.327688e-19
## [304,] 9.367379e-127 1.341366e-25 1.028565e-168 2.611153e-24 1.411251e-09
## [305,] 8.106380e-52 1.384491e-10 5.831280e-100 0.000000e+00 1.813592e-202
## [306,] 2.184447e-81 4.821180e-13 1.074580e-152 0.000000e+00 2.889535e-271
## [307,] 2.413746e-39 5.598849e-73 1.613259e-23 9.999974e-01 6.527852e-08
## [308,] 7.374429e-36 3.014857e-90 1.399562e-04 3.412106e-29 1.669536e-21
## [309,] 0.000000e+00 1.216971e-10 0.000000e+00 4.527777e-71 5.532415e-19
## [310,] 1.709863e-06 1.001498e-31 7.768326e-03 4.399024e-20 4.723228e-15
## [311,] 3.943692e-67 2.482021e-42 1.561237e-76 5.313276e-05 7.200692e-05
## [312,] 1.676149e-03 1.168364e-08 7.846481e-13 1.349475e-46 4.752659e-13
## [313,] 3.249199e-31 1.774824e-08 7.927534e-75 4.281272e-177 7.260524e-66
## [314,] 1.000000e+00 1.884867e-18 1.075653e-40 2.911711e-199 1.447266e-61
## [315,] 1.527896e-15 2.011248e-44 1.089981e-07 1.412757e-18 4.059544e-17
## [316,] 1.000000e+00 2.115159e-22 8.402966e-11 7.804214e-33 3.053289e-19
## [317,] 1.216007e-64 6.578984e-08 1.432870e-121 1.691289e-282 2.756237e-98
## [318,] 9.863318e-11 8.146731e-46 3.994934e-08 8.886634e-25 8.314363e-20
## [319,] 1.319598e-14 2.156697e-42 9.652513e-10 7.266969e-33 3.111254e-40
## [320,] 9.997666e-01 1.533232e-17 2.332305e-04 1.164119e-154 3.090029e-68
## [321,] 1.000000e+00 4.816580e-16 3.189672e-18 1.146915e-152 4.987029e-126
## [322,] 3.780213e-100 4.733005e-87 1.000000e+00 2.002151e-63 6.839785e-101
## [323,] 1.000000e+00 2.538081e-16 9.911089e-15 5.269746e-71 1.523060e-41
## [324,] 7.604189e-142 1.239155e-14 5.634966e-250 0.000000e+00 8.697021e-256
## [325,] 1.166384e-42 1.275924e-106 1.000000e+00 0.000000e+00 1.277667e-79
## [326,] 6.011134e-22 1.218957e-57 4.628155e-13 4.492124e-13 1.242659e-17
## [327,] 2.317296e-77 2.141648e-11 1.357398e-151 0.000000e+00 9.200431e-118
## [328,] 9.998620e-01 1.578395e-23 1.379500e-04 2.123988e-176 1.781551e-38
## [329,] 0.000000e+00 4.270448e-57 0.000000e+00 1.769692e-145 7.840667e-152
## [330,] 0.000000e+00 1.000000e+00 0.000000e+00 0.000000e+00 2.475444e-145
## [331,] 9.993114e-01 5.835251e-23 6.861112e-04 1.740331e-61 9.180213e-61
## [332,] 9.999919e-01 2.097934e-25 8.079168e-06 7.472232e-45 6.680635e-21
## [333,] 3.401394e-34 8.523865e-77 7.439751e-15 2.097069e-18 5.484079e-21
## [334,] 4.128797e-16 5.280468e-50 1.597172e-07 3.304245e-37 1.140299e-43
## [335,] 3.078149e-48 6.663672e-100 1.572983e-14 3.231393e-22 1.329347e-37
## [336,] 9.999999e-01 1.586196e-20 5.359928e-08 4.205486e-240 4.952321e-60
## [337,] 1.000000e+00 5.725261e-13 9.654586e-18 6.490755e-223 8.208905e-84
## [338,] 9.618470e-01 3.832216e-18 1.408664e-06 6.454913e-25 7.429875e-15
## [339,] 1.444468e-124 2.240999e-120 1.000000e+00 1.364924e-110 2.255406e-182
## [340,] 9.999998e-01 2.108187e-25 1.663121e-07 6.574427e-239 1.209432e-31
## [341,] 4.302841e-22 2.257158e-10 6.617815e-52 0.000000e+00 7.320574e-166
## [342,] 9.999998e-01 1.157054e-21 2.273851e-07 6.346385e-73 4.874491e-44
## [343,] 2.462909e-29 2.094154e-16 1.181291e-64 0.000000e+00 1.886997e-261
## [344,] 0.000000e+00 1.381287e-24 0.000000e+00 0.000000e+00 0.000000e+00
## [345,] 0.000000e+00 1.177304e-45 0.000000e+00 9.999945e-01 4.081840e-13
## [346,] 6.022623e-06 1.604729e-12 1.773313e-22 0.000000e+00 1.623993e-141
## [347,] 1.000000e+00 1.920823e-21 4.390005e-09 5.273480e-162 1.843471e-62
## [348,] 9.999969e-01 9.154254e-23 5.918475e-10 6.554872e-34 6.792150e-18
## [349,] 0.000000e+00 3.272772e-11 0.000000e+00 3.715884e-111 6.901718e-33
## [350,] 4.847205e-08 2.681918e-23 9.999962e-01 2.446071e-118 2.333191e-32
## [351,] 7.026913e-20 9.308538e-59 1.762921e-04 5.617541e-27 5.354891e-20
## [352,] 0.000000e+00 1.488389e-59 0.000000e+00 0.000000e+00 0.000000e+00
## [353,] 1.485810e-51 3.034813e-85 1.142717e-19 8.973319e-19 5.200637e-28
## [354,] 9.879136e-01 5.061111e-27 1.195212e-02 2.456390e-31 6.178433e-23
## [355,] 0.000000e+00 5.149583e-10 0.000000e+00 5.308267e-56 1.141155e-21
## [356,] 9.331359e-25 1.778762e-04 7.470005e-43 0.000000e+00 1.012654e-116
## [357,] 1.328172e-31 2.575712e-73 1.017819e-16 2.437245e-13 1.904697e-25
## [358,] 2.050278e-10 9.028116e-40 4.794351e-10 9.871235e-25 8.890342e-25
## [359,] 1.000000e+00 3.446487e-16 3.758612e-10 1.108872e-91 5.072454e-43
## [360,] 0.000000e+00 1.000000e+00 0.000000e+00 0.000000e+00 6.283324e-57
## [361,] 9.998315e-01 1.034875e-24 1.685298e-04 2.494762e-45 1.158495e-34
## [362,] 1.000000e+00 2.093836e-19 2.294936e-11 1.316643e-57 3.994477e-26
## [363,] 0.000000e+00 5.927389e-33 0.000000e+00 4.816814e-04 1.454651e-06
## [364,] 0.000000e+00 3.032513e-15 0.000000e+00 9.070976e-27 7.760148e-04
## [365,] 0.000000e+00 2.207447e-32 0.000000e+00 7.399210e-23 3.251179e-07
## [366,] 5.833976e-12 3.145505e-45 1.122396e-01 3.782807e-36 8.779059e-24
## [367,] 1.000000e+00 9.626105e-13 2.028422e-11 1.655737e-318 4.070573e-139
## [368,] 6.473590e-06 5.437897e-30 1.243879e-08 5.323869e-25 2.177872e-18
## [369,] 0.000000e+00 1.833603e-04 0.000000e+00 5.457343e-125 3.550771e-36
## [370,] 1.000000e+00 1.251818e-15 4.410286e-24 4.713934e-187 5.644310e-83
## [371,] 0.000000e+00 1.461777e-09 0.000000e+00 0.000000e+00 2.428614e-192
## [372,] 2.305360e-04 6.532901e-28 9.527193e-12 5.634955e-13 1.812046e-11
## [373,] 1.671126e-20 4.374791e-53 3.374809e-11 1.262658e-30 1.387244e-34
## [374,] 0.000000e+00 2.597640e-71 0.000000e+00 3.919379e-76 5.165843e-63
## [375,] 2.851922e-15 9.205501e-50 1.476882e-11 1.073721e-34 4.169705e-41
## [376,] 1.000000e+00 1.939610e-22 2.905765e-09 1.977421e-155 2.502941e-134
## [377,] 9.983667e-01 3.179264e-22 1.782450e-05 3.939487e-28 1.845208e-16
##
##
## Slot "model":
## An object of class "VSLCMmodel"
## Slot "g":
## [1] 10
##
## Slot "omega":
## age
## 0
## original_shape_VoxelVolume
## 1
## wavelet.HHL_glcm_ClusterProminence
## 1
## log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis
## 1
## log.sigma.2.0.mm.3D_glcm_Autocorrelation
## 1
## log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis
## 1
## wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis
## 1
## wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis
## 1
## wavelet.HHH_glszm_GrayLevelNonUniformity
## 1
## wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis
## 1
## wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis
## 1
## wavelet.HHH_glcm_ClusterProminence
## 1
## wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis
## 1
## wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis
## 1
## wavelet.LLL_glcm_ClusterProminence
## 1
## wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis
## 1
## wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis
## 1
## wavelet.HHH_glrlm_GrayLevelVariance
## 1
## log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized
## 0
## wavelet.HHH_glcm_Imc1
## 0
## original_glcm_Imc1
## 0
## log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis
## 1
## wavelet.LHH_glszm_GrayLevelNonUniformity
## 1
## wavelet.LHH_firstorder_Skewness
## 1
## wavelet.HHH_glszm_GrayLevelNonUniformityNormalized
## 1
## wavelet.LHH_glcm_Imc1
## 1
## log.sigma.5.0.mm.3D_firstorder_Kurtosis
## 1
## log.sigma.5.0.mm.3D_firstorder_90Percentile
## 1
## wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis
## 1
## wavelet.HLH_glcm_DifferenceAverage
## 1
## wavelet.HHH_firstorder_RootMeanSquared
## 1
## wavelet.LHL_gldm_DependenceVariance
## 1
## gender
## 0
##
## Slot "names.relevant":
## [1] "original_shape_VoxelVolume"
## [2] "wavelet.HHL_glcm_ClusterProminence"
## [3] "log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis"
## [4] "log.sigma.2.0.mm.3D_glcm_Autocorrelation"
## [5] "log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis"
## [6] "wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis"
## [7] "wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis"
## [8] "wavelet.HHH_glszm_GrayLevelNonUniformity"
## [9] "wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis"
## [10] "wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis"
## [11] "wavelet.HHH_glcm_ClusterProminence"
## [12] "wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis"
## [13] "wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis"
## [14] "wavelet.LLL_glcm_ClusterProminence"
## [15] "wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis"
## [16] "wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis"
## [17] "wavelet.HHH_glrlm_GrayLevelVariance"
## [18] "log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis"
## [19] "wavelet.LHH_glszm_GrayLevelNonUniformity"
## [20] "wavelet.LHH_firstorder_Skewness"
## [21] "wavelet.HHH_glszm_GrayLevelNonUniformityNormalized"
## [22] "wavelet.LHH_glcm_Imc1"
## [23] "log.sigma.5.0.mm.3D_firstorder_Kurtosis"
## [24] "log.sigma.5.0.mm.3D_firstorder_90Percentile"
## [25] "wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis"
## [26] "wavelet.HLH_glcm_DifferenceAverage"
## [27] "wavelet.HHH_firstorder_RootMeanSquared"
## [28] "wavelet.LHL_gldm_DependenceVariance"
##
## Slot "names.irrelevant":
## [1] "age"
## [2] "log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized"
## [3] "wavelet.HHH_glcm_Imc1"
## [4] "original_glcm_Imc1"
## [5] "gender"
##
##
## Slot "strategy":
## An object of class "VSLCMstrategy"
## Slot "initModel":
## [1] 13
##
## Slot "vbleSelec":
## [1] TRUE
##
## Slot "crit.varsel":
## [1] "BIC"
##
## Slot "paramEstim":
## [1] TRUE
##
## Slot "parallel":
## [1] TRUE
##
## Slot "nbSmall":
## [1] 250
##
## Slot "iterSmall":
## [1] 20
##
## Slot "nbKeep":
## [1] 50
##
## Slot "iterKeep":
## [1] 1000
##
## Slot "tolKeep":
## [1] 1e-06
##
##
## Slot "param":
## An object of class "VSLCMparam"
## Slot "pi":
## class-1 class-2 class-3 class-4 class-5 class-6 class-7
## 0.09547302 0.13171909 0.14149850 0.09853271 0.03448685 0.16974796 0.05271361
## class-8 class-9 class-10
## 0.07943571 0.08771630 0.10867625
##
## Slot "paramContinuous":
## An object of class "VSLCMparamContinuous"
## Slot "pi":
## numeric(0)
##
## Slot "mu":
## class-1
## age 0.592947330
## original_shape_VoxelVolume 0.182400160
## wavelet.HHL_glcm_ClusterProminence 0.061452114
## log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis 0.012699025
## log.sigma.2.0.mm.3D_glcm_Autocorrelation 0.130223734
## log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis 0.135220648
## wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis 0.166714821
## wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis 0.019288179
## wavelet.HHH_glszm_GrayLevelNonUniformity 0.007778626
## wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis 0.108210162
## wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.252693181
## wavelet.HHH_glcm_ClusterProminence 0.112706297
## wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis 0.069835591
## wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis 0.219106973
## wavelet.LLL_glcm_ClusterProminence 0.596253616
## wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis 0.281023421
## wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.545891969
## wavelet.HHH_glrlm_GrayLevelVariance 0.021431278
## log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized 0.437099112
## wavelet.HHH_glcm_Imc1 0.728416992
## original_glcm_Imc1 0.601881367
## log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis 0.094118245
## wavelet.LHH_glszm_GrayLevelNonUniformity 0.023958333
## wavelet.LHH_firstorder_Skewness 0.461942856
## wavelet.HHH_glszm_GrayLevelNonUniformityNormalized 0.581349133
## wavelet.LHH_glcm_Imc1 0.540702567
## log.sigma.5.0.mm.3D_firstorder_Kurtosis 0.036805326
## log.sigma.5.0.mm.3D_firstorder_90Percentile 0.811532850
## wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis 0.402253074
## wavelet.HLH_glcm_DifferenceAverage 0.242319793
## wavelet.HHH_firstorder_RootMeanSquared 0.649106753
## wavelet.LHL_gldm_DependenceVariance 0.160133557
## class-2 class-3
## age 0.59294733 0.59294733
## original_shape_VoxelVolume 0.81149312 0.66572716
## wavelet.HHL_glcm_ClusterProminence 0.01666209 0.08441970
## log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis 0.46644770 0.27482419
## log.sigma.2.0.mm.3D_glcm_Autocorrelation 0.18916457 0.19603861
## log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis 0.03377438 0.03510574
## wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis 0.78041653 0.62424232
## wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis 0.01147228 0.04869227
## wavelet.HHH_glszm_GrayLevelNonUniformity 0.04285337 0.02489725
## wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis 0.38626493 0.33259568
## wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.01579588 0.02723697
## wavelet.HHH_glcm_ClusterProminence 0.06130083 0.04228104
## wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis 0.37738792 0.31132818
## wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis 0.79220197 0.69205964
## wavelet.LLL_glcm_ClusterProminence 0.46901624 0.58782769
## wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis 0.07544192 0.05137147
## wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.05502594 0.08761967
## wavelet.HHH_glrlm_GrayLevelVariance 0.02479975 0.03948869
## log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized 0.43709911 0.43709911
## wavelet.HHH_glcm_Imc1 0.72841699 0.72841699
## original_glcm_Imc1 0.60188137 0.60188137
## log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis 0.47633175 0.34697861
## wavelet.LHH_glszm_GrayLevelNonUniformity 0.21089854 0.23583231
## wavelet.LHH_firstorder_Skewness 0.50337309 0.46364369
## wavelet.HHH_glszm_GrayLevelNonUniformityNormalized 0.59972918 0.27510729
## wavelet.LHH_glcm_Imc1 0.75334883 0.75655259
## log.sigma.5.0.mm.3D_firstorder_Kurtosis 0.13997095 0.11709519
## log.sigma.5.0.mm.3D_firstorder_90Percentile 0.85759433 0.86255211
## wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis 0.16647430 0.16481646
## wavelet.HLH_glcm_DifferenceAverage 0.19298056 0.25576050
## wavelet.HHH_firstorder_RootMeanSquared 0.79324063 0.80302942
## wavelet.LHL_gldm_DependenceVariance 0.54290811 0.37084631
## class-4
## age 0.59294733
## original_shape_VoxelVolume 0.42906999
## wavelet.HHL_glcm_ClusterProminence 0.12909373
## log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis 0.07921283
## log.sigma.2.0.mm.3D_glcm_Autocorrelation 0.15748087
## log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis 0.05501857
## wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis 0.39079105
## wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis 0.05352157
## wavelet.HHH_glszm_GrayLevelNonUniformity 0.01190964
## wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis 0.24283942
## wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.07488481
## wavelet.HHH_glcm_ClusterProminence 0.07178830
## wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis 0.16329126
## wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis 0.47577911
## wavelet.LLL_glcm_ClusterProminence 0.61954242
## wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis 0.08348892
## wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.18050407
## wavelet.HHH_glrlm_GrayLevelVariance 0.06410141
## log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized 0.43709911
## wavelet.HHH_glcm_Imc1 0.72841699
## original_glcm_Imc1 0.60188137
## log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis 0.16515767
## wavelet.LHH_glszm_GrayLevelNonUniformity 0.10764662
## wavelet.LHH_firstorder_Skewness 0.45610298
## wavelet.HHH_glszm_GrayLevelNonUniformityNormalized 0.28166543
## wavelet.LHH_glcm_Imc1 0.65783084
## log.sigma.5.0.mm.3D_firstorder_Kurtosis 0.04801346
## log.sigma.5.0.mm.3D_firstorder_90Percentile 0.83168076
## wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis 0.21957961
## wavelet.HLH_glcm_DifferenceAverage 0.28216919
## wavelet.HHH_firstorder_RootMeanSquared 0.77821698
## wavelet.LHL_gldm_DependenceVariance 0.26371936
## class-5
## age 0.592947330
## original_shape_VoxelVolume 0.768754847
## wavelet.HHL_glcm_ClusterProminence 0.278383328
## log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis 0.336038776
## log.sigma.2.0.mm.3D_glcm_Autocorrelation 0.620496352
## log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis 0.030124733
## wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis 0.812454469
## wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis 0.172133011
## wavelet.HHH_glszm_GrayLevelNonUniformity 0.194456109
## wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis 0.758225808
## wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.003391303
## wavelet.HHH_glcm_ClusterProminence 0.155803488
## wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis 0.757026242
## wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis 0.725037123
## wavelet.LLL_glcm_ClusterProminence 0.647329690
## wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis 0.015361329
## wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.021664537
## wavelet.HHH_glrlm_GrayLevelVariance 0.215598207
## log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized 0.437099112
## wavelet.HHH_glcm_Imc1 0.728416992
## original_glcm_Imc1 0.601881367
## log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis 0.795688381
## wavelet.LHH_glszm_GrayLevelNonUniformity 0.264553884
## wavelet.LHH_firstorder_Skewness 0.590363795
## wavelet.HHH_glszm_GrayLevelNonUniformityNormalized 0.204551227
## wavelet.LHH_glcm_Imc1 0.656980709
## log.sigma.5.0.mm.3D_firstorder_Kurtosis 0.184367384
## log.sigma.5.0.mm.3D_firstorder_90Percentile 0.870846945
## wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis 0.030021356
## wavelet.HLH_glcm_DifferenceAverage 0.296925173
## wavelet.HHH_firstorder_RootMeanSquared 0.803744679
## wavelet.LHL_gldm_DependenceVariance 0.381005891
## class-6 class-7
## age 0.59294733 0.59294733
## original_shape_VoxelVolume 0.48394008 0.26369750
## wavelet.HHL_glcm_ClusterProminence 0.02738435 0.34929334
## log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis 0.12262099 0.02994923
## log.sigma.2.0.mm.3D_glcm_Autocorrelation 0.15276634 0.22342747
## log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis 0.03131222 0.05211724
## wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis 0.47605673 0.23035946
## wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis 0.01100378 0.11137200
## wavelet.HHH_glszm_GrayLevelNonUniformity 0.01907421 0.03429935
## wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis 0.24190950 0.27324584
## wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.07170625 0.12085868
## wavelet.HHH_glcm_ClusterProminence 0.09698784 0.14548721
## wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis 0.20324685 0.15268300
## wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis 0.50443790 0.28248693
## wavelet.LLL_glcm_ClusterProminence 0.57459928 0.70506151
## wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis 0.15572060 0.09694203
## wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.18370202 0.32145924
## wavelet.HHH_glrlm_GrayLevelVariance 0.02449128 0.28215316
## log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized 0.43709911 0.43709911
## wavelet.HHH_glcm_Imc1 0.72841699 0.72841699
## original_glcm_Imc1 0.60188137 0.60188137
## log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis 0.21063055 0.17138698
## wavelet.LHH_glszm_GrayLevelNonUniformity 0.08395623 0.05946962
## wavelet.LHH_firstorder_Skewness 0.48129001 0.48277410
## wavelet.HHH_glszm_GrayLevelNonUniformityNormalized 0.61870779 0.30870918
## wavelet.LHH_glcm_Imc1 0.63531875 0.50303798
## log.sigma.5.0.mm.3D_firstorder_Kurtosis 0.06177479 0.04447679
## log.sigma.5.0.mm.3D_firstorder_90Percentile 0.82256970 0.80476812
## wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis 0.28687876 0.22083911
## wavelet.HLH_glcm_DifferenceAverage 0.13828297 0.51168456
## wavelet.HHH_firstorder_RootMeanSquared 0.77594061 0.73664121
## wavelet.LHL_gldm_DependenceVariance 0.44164400 0.15207259
## class-8 class-9
## age 0.59294733 0.59294733
## original_shape_VoxelVolume 0.62990992 0.82977756
## wavelet.HHL_glcm_ClusterProminence 0.02962798 0.09585072
## log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis 0.40042816 0.40643136
## log.sigma.2.0.mm.3D_glcm_Autocorrelation 0.10760547 0.29103737
## log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis 0.10789365 0.03023413
## wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis 0.59307348 0.78018694
## wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis 0.01845431 0.06033631
## wavelet.HHH_glszm_GrayLevelNonUniformity 0.01691500 0.08987769
## wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis 0.22715819 0.46861938
## wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.04385011 0.01008903
## wavelet.HHH_glcm_ClusterProminence 0.04327149 0.03972138
## wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis 0.23526681 0.40450751
## wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis 0.65691038 0.79666572
## wavelet.LLL_glcm_ClusterProminence 0.39876760 0.65170533
## wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis 0.11691107 0.02980561
## wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.15489808 0.04342778
## wavelet.HHH_glrlm_GrayLevelVariance 0.02463421 0.05480131
## log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized 0.43709911 0.43709911
## wavelet.HHH_glcm_Imc1 0.72841699 0.72841699
## original_glcm_Imc1 0.60188137 0.60188137
## log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis 0.31453414 0.54211027
## wavelet.LHH_glszm_GrayLevelNonUniformity 0.11022493 0.38076617
## wavelet.LHH_firstorder_Skewness 0.43614026 0.46475167
## wavelet.HHH_glszm_GrayLevelNonUniformityNormalized 0.60359674 0.22377302
## wavelet.LHH_glcm_Imc1 0.78125526 0.72560583
## log.sigma.5.0.mm.3D_firstorder_Kurtosis 0.13124329 0.16837994
## log.sigma.5.0.mm.3D_firstorder_90Percentile 0.85969238 0.87868358
## wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis 0.34605208 0.09443876
## wavelet.HLH_glcm_DifferenceAverage 0.21120356 0.28252443
## wavelet.HHH_firstorder_RootMeanSquared 0.80470139 0.81929209
## wavelet.LHL_gldm_DependenceVariance 0.29594720 0.42080910
## class-10
## age 0.59294733
## original_shape_VoxelVolume 0.70643519
## wavelet.HHL_glcm_ClusterProminence 0.21013181
## log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis 0.25734172
## log.sigma.2.0.mm.3D_glcm_Autocorrelation 0.26854376
## log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis 0.02595008
## wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis 0.61142050
## wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis 0.10645864
## wavelet.HHH_glszm_GrayLevelNonUniformity 0.22198647
## wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis 0.38998134
## wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.02379659
## wavelet.HHH_glcm_ClusterProminence 0.06682110
## wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis 0.31956806
## wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis 0.63432021
## wavelet.LLL_glcm_ClusterProminence 0.71292840
## wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis 0.03310723
## wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.07815821
## wavelet.HHH_glrlm_GrayLevelVariance 0.15571656
## log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized 0.43709911
## wavelet.HHH_glcm_Imc1 0.72841699
## original_glcm_Imc1 0.60188137
## log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis 0.38181431
## wavelet.LHH_glszm_GrayLevelNonUniformity 0.41689074
## wavelet.LHH_firstorder_Skewness 0.45299001
## wavelet.HHH_glszm_GrayLevelNonUniformityNormalized 0.33528993
## wavelet.LHH_glcm_Imc1 0.70780020
## log.sigma.5.0.mm.3D_firstorder_Kurtosis 0.16039029
## log.sigma.5.0.mm.3D_firstorder_90Percentile 0.88183012
## wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis 0.09669403
## wavelet.HLH_glcm_DifferenceAverage 0.38491410
## wavelet.HHH_firstorder_RootMeanSquared 0.83525344
## wavelet.LHL_gldm_DependenceVariance 0.29462514
##
## Slot "sd":
## class-1
## age 0.172062923
## original_shape_VoxelVolume 0.087668545
## wavelet.HHL_glcm_ClusterProminence 0.041846299
## log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis 0.017845836
## log.sigma.2.0.mm.3D_glcm_Autocorrelation 0.072098929
## log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis 0.207970485
## wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis 0.101456140
## wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis 0.012956788
## wavelet.HHH_glszm_GrayLevelNonUniformity 0.007488875
## wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis 0.051721046
## wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.189919906
## wavelet.HHH_glcm_ClusterProminence 0.033719395
## wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis 0.040927882
## wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis 0.096417875
## wavelet.LLL_glcm_ClusterProminence 0.111568014
## wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis 0.164450559
## wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.205749328
## wavelet.HHH_glrlm_GrayLevelVariance 0.004298556
## log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized 0.154347485
## wavelet.HHH_glcm_Imc1 0.174759549
## original_glcm_Imc1 0.120260178
## log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis 0.055778544
## wavelet.LHH_glszm_GrayLevelNonUniformity 0.017151639
## wavelet.LHH_firstorder_Skewness 0.079498039
## wavelet.HHH_glszm_GrayLevelNonUniformityNormalized 0.079650559
## wavelet.LHH_glcm_Imc1 0.184566389
## log.sigma.5.0.mm.3D_firstorder_Kurtosis 0.013334064
## log.sigma.5.0.mm.3D_firstorder_90Percentile 0.104660169
## wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis 0.255722813
## wavelet.HLH_glcm_DifferenceAverage 0.165072213
## wavelet.HHH_firstorder_RootMeanSquared 0.229052987
## wavelet.LHL_gldm_DependenceVariance 0.135054827
## class-2
## age 0.172062923
## original_shape_VoxelVolume 0.097419651
## wavelet.HHL_glcm_ClusterProminence 0.012147985
## log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis 0.206874791
## log.sigma.2.0.mm.3D_glcm_Autocorrelation 0.058563546
## log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis 0.018012107
## wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis 0.083053815
## wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis 0.006195976
## wavelet.HHH_glszm_GrayLevelNonUniformity 0.025898716
## wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis 0.097060434
## wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.010675993
## wavelet.HHH_glcm_ClusterProminence 0.026482478
## wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis 0.092974020
## wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis 0.082442324
## wavelet.LLL_glcm_ClusterProminence 0.150859294
## wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis 0.035664222
## wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.024791153
## wavelet.HHH_glrlm_GrayLevelVariance 0.000387613
## log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized 0.154347485
## wavelet.HHH_glcm_Imc1 0.174759549
## original_glcm_Imc1 0.120260178
## log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis 0.090675128
## wavelet.LHH_glszm_GrayLevelNonUniformity 0.091837303
## wavelet.LHH_firstorder_Skewness 0.088138493
## wavelet.HHH_glszm_GrayLevelNonUniformityNormalized 0.140203154
## wavelet.LHH_glcm_Imc1 0.086461391
## log.sigma.5.0.mm.3D_firstorder_Kurtosis 0.146402226
## log.sigma.5.0.mm.3D_firstorder_90Percentile 0.013411301
## wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis 0.055881150
## wavelet.HLH_glcm_DifferenceAverage 0.035345903
## wavelet.HHH_firstorder_RootMeanSquared 0.021639839
## wavelet.LHL_gldm_DependenceVariance 0.187271107
## class-3 class-4
## age 0.17206292 0.17206292
## original_shape_VoxelVolume 0.08706836 0.08144505
## wavelet.HHL_glcm_ClusterProminence 0.04504945 0.07276689
## log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis 0.13313139 0.08253297
## log.sigma.2.0.mm.3D_glcm_Autocorrelation 0.07499478 0.06605953
## log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis 0.02258875 0.09466626
## wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis 0.06091455 0.09863477
## wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis 0.02390365 0.02791937
## wavelet.HHH_glszm_GrayLevelNonUniformity 0.01912562 0.01296977
## wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis 0.10468808 0.07407924
## wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.01231444 0.04096670
## wavelet.HHH_glcm_ClusterProminence 0.02269006 0.03102674
## wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis 0.07931136 0.06241333
## wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis 0.07821007 0.09681111
## wavelet.LLL_glcm_ClusterProminence 0.12235836 0.09254162
## wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis 0.01997631 0.03171961
## wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.03719342 0.06707354
## wavelet.HHH_glrlm_GrayLevelVariance 0.01470931 0.04917207
## log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized 0.15434749 0.15434749
## wavelet.HHH_glcm_Imc1 0.17475955 0.17475955
## original_glcm_Imc1 0.12026018 0.12026018
## log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis 0.09686305 0.05713154
## wavelet.LHH_glszm_GrayLevelNonUniformity 0.12128520 0.04930763
## wavelet.LHH_firstorder_Skewness 0.06158355 0.06158615
## wavelet.HHH_glszm_GrayLevelNonUniformityNormalized 0.17789692 0.20930050
## wavelet.LHH_glcm_Imc1 0.08502883 0.11889117
## log.sigma.5.0.mm.3D_firstorder_Kurtosis 0.07142922 0.02523829
## log.sigma.5.0.mm.3D_firstorder_90Percentile 0.02532476 0.05306318
## wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis 0.06961034 0.15426006
## wavelet.HLH_glcm_DifferenceAverage 0.04470681 0.08600772
## wavelet.HHH_firstorder_RootMeanSquared 0.03212384 0.08085622
## wavelet.LHL_gldm_DependenceVariance 0.16438097 0.17206579
## class-5
## age 0.172062923
## original_shape_VoxelVolume 0.093330474
## wavelet.HHL_glcm_ClusterProminence 0.246374643
## log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis 0.158996943
## log.sigma.2.0.mm.3D_glcm_Autocorrelation 0.188066041
## log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis 0.018855211
## wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis 0.072482528
## wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis 0.245498774
## wavelet.HHH_glszm_GrayLevelNonUniformity 0.159412796
## wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis 0.120442129
## wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.002731993
## wavelet.HHH_glcm_ClusterProminence 0.249441386
## wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis 0.148436638
## wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis 0.070285526
## wavelet.LLL_glcm_ClusterProminence 0.143812978
## wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis 0.011417910
## wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.017122798
## wavelet.HHH_glrlm_GrayLevelVariance 0.205093797
## log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized 0.154347485
## wavelet.HHH_glcm_Imc1 0.174759549
## original_glcm_Imc1 0.120260178
## log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis 0.109376689
## wavelet.LHH_glszm_GrayLevelNonUniformity 0.147934456
## wavelet.LHH_firstorder_Skewness 0.136967364
## wavelet.HHH_glszm_GrayLevelNonUniformityNormalized 0.135550912
## wavelet.LHH_glcm_Imc1 0.099448018
## log.sigma.5.0.mm.3D_firstorder_Kurtosis 0.105161114
## log.sigma.5.0.mm.3D_firstorder_90Percentile 0.016003194
## wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis 0.017069514
## wavelet.HLH_glcm_DifferenceAverage 0.068462683
## wavelet.HHH_firstorder_RootMeanSquared 0.032357371
## wavelet.LHL_gldm_DependenceVariance 0.190136836
## class-6
## age 0.1720629233
## original_shape_VoxelVolume 0.1026363936
## wavelet.HHL_glcm_ClusterProminence 0.0166793443
## log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis 0.0913240321
## log.sigma.2.0.mm.3D_glcm_Autocorrelation 0.0635352698
## log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis 0.0185121589
## wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis 0.0973989715
## wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis 0.0056840723
## wavelet.HHH_glszm_GrayLevelNonUniformity 0.0138844496
## wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis 0.0889376032
## wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.0414680844
## wavelet.HHH_glcm_ClusterProminence 0.0286321744
## wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis 0.0800728239
## wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis 0.0875243555
## wavelet.LLL_glcm_ClusterProminence 0.1236917912
## wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis 0.0670042078
## wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.0996980723
## wavelet.HHH_glrlm_GrayLevelVariance 0.0001483826
## log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized 0.1543474853
## wavelet.HHH_glcm_Imc1 0.1747595493
## original_glcm_Imc1 0.1202601782
## log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis 0.0875757294
## wavelet.LHH_glszm_GrayLevelNonUniformity 0.0557150896
## wavelet.LHH_firstorder_Skewness 0.0648487551
## wavelet.HHH_glszm_GrayLevelNonUniformityNormalized 0.1115339232
## wavelet.LHH_glcm_Imc1 0.1193230941
## log.sigma.5.0.mm.3D_firstorder_Kurtosis 0.0430996336
## log.sigma.5.0.mm.3D_firstorder_90Percentile 0.0559245698
## wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis 0.2062236112
## wavelet.HLH_glcm_DifferenceAverage 0.0425902952
## wavelet.HHH_firstorder_RootMeanSquared 0.0486900068
## wavelet.LHL_gldm_DependenceVariance 0.2082893550
## class-7
## age 0.17206292
## original_shape_VoxelVolume 0.10646075
## wavelet.HHL_glcm_ClusterProminence 0.15598487
## log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis 0.05933084
## log.sigma.2.0.mm.3D_glcm_Autocorrelation 0.11695960
## log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis 0.02721794
## wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis 0.14725412
## wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis 0.05132051
## wavelet.HHH_glszm_GrayLevelNonUniformity 0.04319721
## wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis 0.12890924
## wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.07472393
## wavelet.HHH_glcm_ClusterProminence 0.08282039
## wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis 0.07403798
## wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis 0.09965683
## wavelet.LLL_glcm_ClusterProminence 0.15699104
## wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis 0.05608713
## wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.20344098
## wavelet.HHH_glrlm_GrayLevelVariance 0.25848226
## log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized 0.15434749
## wavelet.HHH_glcm_Imc1 0.17475955
## original_glcm_Imc1 0.12026018
## log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis 0.09830210
## wavelet.LHH_glszm_GrayLevelNonUniformity 0.03993056
## wavelet.LHH_firstorder_Skewness 0.16974296
## wavelet.HHH_glszm_GrayLevelNonUniformityNormalized 0.16719436
## wavelet.LHH_glcm_Imc1 0.19857503
## log.sigma.5.0.mm.3D_firstorder_Kurtosis 0.03846053
## log.sigma.5.0.mm.3D_firstorder_90Percentile 0.20568195
## wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis 0.11369051
## wavelet.HLH_glcm_DifferenceAverage 0.21439511
## wavelet.HHH_firstorder_RootMeanSquared 0.18199168
## wavelet.LHL_gldm_DependenceVariance 0.15438412
## class-8
## age 0.1720629233
## original_shape_VoxelVolume 0.1464280626
## wavelet.HHL_glcm_ClusterProminence 0.0298631737
## log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis 0.2200425307
## log.sigma.2.0.mm.3D_glcm_Autocorrelation 0.0508936556
## log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis 0.1202159982
## wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis 0.1158499466
## wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis 0.0146432651
## wavelet.HHH_glszm_GrayLevelNonUniformity 0.0144763405
## wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis 0.1033219309
## wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.0284360240
## wavelet.HHH_glcm_ClusterProminence 0.0245804925
## wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis 0.0608849413
## wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis 0.1290545508
## wavelet.LLL_glcm_ClusterProminence 0.1641064498
## wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis 0.0614727058
## wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.0743965880
## wavelet.HHH_glrlm_GrayLevelVariance 0.0001226549
## log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized 0.1543474853
## wavelet.HHH_glcm_Imc1 0.1747595493
## original_glcm_Imc1 0.1202601782
## log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis 0.1145259728
## wavelet.LHH_glszm_GrayLevelNonUniformity 0.0906209109
## wavelet.LHH_firstorder_Skewness 0.0736772554
## wavelet.HHH_glszm_GrayLevelNonUniformityNormalized 0.0879280964
## wavelet.LHH_glcm_Imc1 0.1114175808
## log.sigma.5.0.mm.3D_firstorder_Kurtosis 0.0958984323
## log.sigma.5.0.mm.3D_firstorder_90Percentile 0.0214334268
## wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis 0.2302072836
## wavelet.HLH_glcm_DifferenceAverage 0.0470797997
## wavelet.HHH_firstorder_RootMeanSquared 0.0205277960
## wavelet.LHL_gldm_DependenceVariance 0.1483001944
## class-9
## age 0.172062923
## original_shape_VoxelVolume 0.066247516
## wavelet.HHL_glcm_ClusterProminence 0.043488992
## log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis 0.147335985
## log.sigma.2.0.mm.3D_glcm_Autocorrelation 0.115463216
## log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis 0.027149862
## wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis 0.062765968
## wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis 0.030729873
## wavelet.HHH_glszm_GrayLevelNonUniformity 0.054040204
## wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis 0.101083893
## wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.005501061
## wavelet.HHH_glcm_ClusterProminence 0.028410596
## wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis 0.068156824
## wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis 0.059384313
## wavelet.LLL_glcm_ClusterProminence 0.130663454
## wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis 0.014991562
## wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.018206339
## wavelet.HHH_glrlm_GrayLevelVariance 0.020492498
## log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized 0.154347485
## wavelet.HHH_glcm_Imc1 0.174759549
## original_glcm_Imc1 0.120260178
## log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis 0.097540713
## wavelet.LHH_glszm_GrayLevelNonUniformity 0.129393316
## wavelet.LHH_firstorder_Skewness 0.048692482
## wavelet.HHH_glszm_GrayLevelNonUniformityNormalized 0.131376939
## wavelet.LHH_glcm_Imc1 0.095233997
## log.sigma.5.0.mm.3D_firstorder_Kurtosis 0.118916877
## log.sigma.5.0.mm.3D_firstorder_90Percentile 0.026860348
## wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis 0.018356695
## wavelet.HLH_glcm_DifferenceAverage 0.040039667
## wavelet.HHH_firstorder_RootMeanSquared 0.026725894
## wavelet.LHL_gldm_DependenceVariance 0.162380302
## class-10
## age 0.17206292
## original_shape_VoxelVolume 0.10537904
## wavelet.HHL_glcm_ClusterProminence 0.07956308
## log.sigma.5.0.mm.3D_glszm_LargeAreaLowGrayLevelEmphasis 0.17065986
## log.sigma.2.0.mm.3D_glcm_Autocorrelation 0.07942438
## log.sigma.5.0.mm.3D_glszm_SmallAreaLowGrayLevelEmphasis 0.01880629
## wavelet.LHL_glszm_LargeAreaHighGrayLevelEmphasis 0.07451149
## wavelet.HHL_gldm_SmallDependenceHighGrayLevelEmphasis 0.03600364
## wavelet.HHH_glszm_GrayLevelNonUniformity 0.21802687
## wavelet.HLL_glrlm_LongRunHighGrayLevelEmphasis 0.09007467
## wavelet.LHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.01099469
## wavelet.HHH_glcm_ClusterProminence 0.04090347
## wavelet.LLH_glrlm_LongRunHighGrayLevelEmphasis 0.07497035
## wavelet.HHH_glszm_LargeAreaHighGrayLevelEmphasis 0.08118004
## wavelet.LLL_glcm_ClusterProminence 0.12415037
## wavelet.HHL_glszm_SmallAreaLowGrayLevelEmphasis 0.01260770
## wavelet.HHL_gldm_SmallDependenceLowGrayLevelEmphasis 0.02985670
## wavelet.HHH_glrlm_GrayLevelVariance 0.09868326
## log.sigma.5.0.mm.3D_glszm_SizeZoneNonUniformityNormalized 0.15434749
## wavelet.HHH_glcm_Imc1 0.17475955
## original_glcm_Imc1 0.12026018
## log.sigma.2.0.mm.3D_glrlm_LongRunHighGrayLevelEmphasis 0.09282427
## wavelet.LHH_glszm_GrayLevelNonUniformity 0.21790669
## wavelet.LHH_firstorder_Skewness 0.04491834
## wavelet.HHH_glszm_GrayLevelNonUniformityNormalized 0.08701525
## wavelet.LHH_glcm_Imc1 0.11982805
## log.sigma.5.0.mm.3D_firstorder_Kurtosis 0.13248173
## log.sigma.5.0.mm.3D_firstorder_90Percentile 0.03111954
## wavelet.HLH_glszm_SmallAreaLowGrayLevelEmphasis 0.06193935
## wavelet.HLH_glcm_DifferenceAverage 0.09322424
## wavelet.HHH_firstorder_RootMeanSquared 0.03465005
## wavelet.LHL_gldm_DependenceVariance 0.17939141
##
##
## Slot "paramInteger":
## An object of class "VSLCMparamInteger"
## Slot "pi":
## numeric(0)
##
## Slot "lambda":
## [,1]
## [1,] NA
##
##
## Slot "paramCategorical":
## An object of class "VSLCMparamCategorical"
## Slot "pi":
## numeric(0)
##
## Slot "alpha":
## $gender
## female male
## class-1 0.3129973 0.6870027
## class-2 0.3129973 0.6870027
## class-3 0.3129973 0.6870027
## class-4 0.3129973 0.6870027
## class-5 0.3129973 0.6870027
## class-6 0.3129973 0.6870027
## class-7 0.3129973 0.6870027
## class-8 0.3129973 0.6870027
## class-9 0.3129973 0.6870027
## class-10 0.3129973 0.6870027